Model creation support system, model creation support method, and model creation support program

ABSTRACT

A model creation support system includes a model managing part that acquires and accumulates an identifier of a model, data indicating a phenomenon, data indicating a regression equation, and evaluation data from an information processing apparatus that predicts or analyzes the phenomenon, using a model represented by the regression equation, a factor value extracting part that generates and records factor value data indicating the degree to which the factor contributes to the model from the accumulated data, and a model proposing part that refers to the factor value data based on a request for supporting model creation and model condition data received from the information processing apparatus, thereby generating and outputting support data containing a factor capable of contributing to the model to the information processing apparatus. Thus, information on factors of the model can be accumulated and utilized for enhancement of the fitting degree of the model.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a computer system supporting thecreation of a model used for predicting a phenomenon that changes withthe passage of time, such as monthly sales in a store.

2. Description of Related Art

Recently, along with the proliferation of a sensor network, it isbecoming easy to collect data indicating various industrial phenomena(for example, sales, environment, a machine, a vital phenomenon). Suchdata can be used as useful information in various spots such as a retailstore and a maintenance spot. Then, an attempt has been made so as toapply a statistic model (mathematical expression) to such data, therebyunderstanding the nature of a phenomenon indicated by the data, andfurther predicting a future phenomenon and finding a change incharacteristics in an early stage.

One example of such attempts is the creation of a model obtained byperforming a regression analysis with respect to data indicating a pastphenomenon and expressing the phenomenon by a regression equation. Theuse of the model enables a past phenomenon to be analyzed or a futurephenomenon to be predicted. In the regression equation, a phenomenon tobe a target is expressed by an objective variable, and a factorinfluencing the phenomenon is expressed by an explanatory variable. Theobjective variable is also referred to as a dependent variable, aresponse variable, an explained variable, or a criterion variable. Theexplanatory variable is also referred to as an independent variable or acovariate. The following Expression (1) is an example of a regressionequation of linear multiple regression. In the following Expression (1),Y is an objective variable, X1 and X2 are explanatory variables, and a,b and c are constants. In particular, b and c are called partialregression coefficients.Y=a+b·X1+c·X2  (1)

As an example, in the case of predicting sales in a store, the objectivevariable Y, the explanatory variable X1, and the explanatory variable X2are respectively defined as a predicted value of sales, a numericalvalue representing an assortment degree of goods, and an average priceof goods in the above Expression (1). In this case, the constants “a”,“b”, and “c” can be obtained, using data on past sales, assortments ofgoods, and average prices in a plurality of stores (for example, aplurality of chain stores). As a result, for example, a store keeper cancompare the respective sales contribution degrees of an assortment and aprice of goods in accordance with Expression (1), and can also predict asales from the assortment and the price of goods.

Thus, in the case of creating a regression equation of a model foranalyzing or predicting a phenomenon, it is important to determine whatis used as an explanatory variable to be a factor for explaining thephenomenon. This is because a fitting degree varies depending upon howto select an explanatory variable. The determination of such anappropriate explanatory variable cannot help depending upon theexperiment, hunch, and trial and error of an analyzer at a spot.

In order to obtain an optimum model, a prediction apparatus has beendisclosed, which calculates an error between a predicted value and anactually measured value in a predicted model, and updates the predictedmodel when the error is large (see, for example, JP 9-95917 A). Asanother example, a method for selecting a predicted model to beprovided, using prediction data in the case of applying time-seriesachievement data to a plurality of predicted models, has been disclosed(see, for example, JP 2001-22729 A).

However, JP 9-95917 A and JP 2001-22729 A disclose a predictionapparatus and method for modifying a predicted model regarding aparticular phenomenon, and do not provide a mechanism of accumulatingfactors of a predicted model to be used in various informationprocessing apparatuses and utilizing the factors for creating ormodifying the predicted model in various information processingapparatuses. Furthermore, along with the recent proliferation of anetwork, it is expected that a system for accumulating and utilizinginformation on factors of a predicted model will be demanded more in thefuture.

SUMMARY OF THE INVENTION

Therefore, with the foregoing in mind, it is an object of the presentinvention to provide a model creation support system, a model creationsupport method, and a model creation support program that accumulateinformation on factors of a model and utilize the information forenhancing a fitting degree of the model.

A model creation support system disclosed herein is capable of accessingan information processing apparatus that predicts or analyzes aphenomenon to be a target for prediction or analysis, using a model thatis data indicating the phenomenon as an objective variable in aregression equation utilizing an explanatory variable corresponding to afactor contributing to the phenomenon. The model creation support systemincludes: a model managing part that acquires an identifier of themodel, phenomenon data indicating the phenomenon to be a target of themodel, the regression equation of the model, factor data indicating thefactor corresponding to the explanatory variable included in theregression equation, and evaluation data containing a fitting degree ofthe model from the information processing apparatus, and accumulatesthem in a model recording part accessible from the model creationsupport system; a factor value extracting part that, regarding at leastone model, refers to the factor data corresponding to the explanatoryvariable in the regression equation of the model and the evaluation dataon the model in the data accumulated in the model recording part,thereby generating factor value data indicating a degree to which thefactor indicated by the factor data contributes to enhancement of thefitting degree of the model and recording the factor value data in afactor value recording part accessible from the model creation supportsystem so that the factor value data is associated with the phenomenondata indicating the phenomenon to be a target of the model; a conditionacquiring part that receives a request for supporting model creationfrom the information processing apparatus, and further receives an inputof model condition data containing data indicating a phenomenon to be atarget of a requested model; and a model proposing part that matches thedata indicating the phenomenon to be a target of the requested modelcontained in the model condition data with the phenomenon dataassociated with the factor value data in the factor value recordingpart, thereby extracting a recommended factor capable of contributing tothe enhancement of a fitting degree of the requested model andoutputting support data containing data indicating the extractedrecommended factor to the information processing apparatus.

The data on a model used in the information processing apparatus isacquired by the model managing part and accumulated in the modelrecording part. The data to be accumulated contains an identifier of themodel, phenomenon data indicating a target phenomenon of the model, aregression equation, factor data, and evaluation data. The factor valueextracting part refers to the accumulated factor data and the evaluationdata, thereby generating factor value data indicating the degree towhich a factor corresponding to the regression equation of the modelcontributes to the enhancement of a fitting degree of the model, andrecording the factor value data in the factor value recording part sothat the factor value data is associated with data indicating the targetphenomenon of the model. Therefore, the model proposing part can referto factor value data on a factor corresponding to a phenomenon specifiedby model condition data acquired by the condition acquiring part amongthe factor value data recorded in the factor value recording part. Themodel proposing part can obtain information indicating the contributiondegree of a factor capable of contributing to the enhancement of afitting degree of a requested model, with reference to the factor valuedata. Thus, the model proposing part can extract an appropriaterecommended factor capable of contributing to the enhancement of afitting degree of the requested model, and output the recommended factorto the information processing apparatus so that it is included insupport data. Consequently, the information processing apparatus islikely to generate a model with the fitting degree enhanced, using theappropriate recommended factor contained in the support data. Thus, themodel creation support system can accumulate information regarding thefactors of a model and utilize the information for enhancing the fittingdegree of the model.

The factor can be an event to be an element of a phenomenon, and thefactor expressed as a variable in a regression equation is anexplanatory variable (which may also be referred to as an explanatoryvariate).

According to the invention disclosed herein, information on the factorsof a model can be shared and utilized.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram showing a configuration of a modelcreation support system of Embodiment 1.

FIG. 2 is a diagram showing an example of a structure of data to berecorded in a model instance DB.

FIG. 3 is a diagram showing an example of a structure of data to berecorded in an objective variable DB.

FIG. 4 is a diagram showing an example of a structure of data to berecorded in an explanatory variable DB.

FIG. 5 is a diagram showing an example of a structure of data to berecorded in a factor value DB.

FIG. 6 is a flowchart illustrating an operation example of a modelmanaging part.

FIG. 7 is a flowchart illustrating an operation example when a modelproposing part receives a request for creating a replacement model.

FIG. 8 is a diagram showing an example of a screen display of similarfactor space data in an information processing apparatus.

FIG. 9 is a diagram showing an example of a screen display of similarmodel space data in the information processing apparatus.

FIG. 10 is a flowchart illustrating an operation example of aninner-model factor value extracting part.

FIG. 11 is a flowchart illustrating the detail of processing in Op42shown in FIG. 10.

FIG. 12 is a diagram conceptually showing a model before a factor ischanged and a model after the factor is changed.

FIG. 13 is a diagram conceptually showing a model before a factor ischanged and a model after the factor is changed.

FIG. 14 is a flowchart illustrating the detail of processing in Op45shown in FIG. 10.

FIG. 15 is a diagram conceptually showing models before and after achange in the case where an analysis application period is changed.

FIG. 16 is a diagram showing models before and after a change in thecase where only a predicted application period is changed.

FIG. 17 is a flowchart illustrating an operation example of aninner-model factor value extracting part.

FIG. 18 is a flowchart illustrating a specific example of processing inOp62 shown in FIG. 17.

FIG. 19 is a conceptual diagram showing an example of a group of modelsthat have been applied for a long period of time.

FIG. 20 is a flowchart illustrating a specific example of processing inOp64 shown in FIG. 17.

FIG. 21 is a conceptual diagram showing an example of a group of modelsapplied in the same period.

FIG. 22 is a flowchart illustrating an example of processing of creatingsimilar factor space data.

FIG. 23 is a flowchart of the calculation of a distance d_(v) betweenfactor values of a factor “a” and a factor “b”.

FIG. 24 is a flowchart illustrating an example of processing of creatingsimilar model space data.

FIG. 25 is a flowchart illustrating the detail of processing in Op1603shown in FIG. 24.

FIG. 26 is a flowchart illustrating an example of the calculation of aninter-model distance d_(m) of a model Ma and a model Mb.

FIG. 27 is a diagram conceptually showing distances between respectiveattributes of models.

FIG. 28 is a flowchart illustrating the detail of processing in Op1605shown in FIG. 24.

FIG. 29 is a flowchart illustrating the detail of processing in Op1607shown in FIG. 24.

FIG. 30 is a diagram conceptually showing an example of a group ofsimilar factors in which a distance from a factor to be replaced issmaller than a threshold value.

FIG. 31 is display examples of screens for requesting informationindicating conditions of a replacement model.

FIG. 32 shows examples of screens which a condition acquiring partallows an information processing apparatus to display.

FIG. 33 is a flowchart illustrating an example of novel model creationprocessing.

FIG. 34 is a diagram showing examples of screens in the case where thecategory (field) of a phenomenon and the kind of the phenomenon aredisplayed so as to be selected.

FIG. 35 illustrates a path chart of a model M1 shown in FIG. 12 and anequation thereof.

FIG. 36 illustrates a path chart including a relationship of explanatoryvariables X₀ to X₃ and an equation thereof.

FIG. 37 is a diagram showing an example of a record storing inter-factorlinks and link weights in a model instance DB.

FIG. 38 is a functional block diagram showing a configuration of a modelcreation support system in Embodiment 3.

FIG. 39 is a diagram showing an example of data contents to be recordedas event information.

FIG. 40 is a flowchart illustrating an operation example of an eventfactor creating part.

FIG. 41 is a flowchart illustrating an operation example of a factorprocuring part.

FIG. 42 is a diagram showing examples of element values of “GW”, “latespring” “beginning of summer” and “late spring to beginning of summer”.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, the present invention will be described by way ofillustrative embodiments with reference to the drawings.

In an embodiment of the present invention, it is preferred that thefactor value extracting part includes: an inner-model factor valueextracting part that detects a transition of the factor data indicatingthe factor corresponding to the explanatory variable included in theregression equation of the model regarding at least one phenomenon and atransition of the evaluation data from the data accumulated in the modelrecording part, thereby generating factor value data indicating a degreeto which a factor corresponding to an explanatory variable added ordeleted with respect to the regression equation of the model contributesto the enhancement of a fitting degree of the model and records thefactor value data in the factor value recording part; and an inter-modelfactor value extracting part that refers to factor data indicatingfactors corresponding explanatory variables included in regressionequations of a plurality of models and evaluation data in the dataaccumulated in the model recording part, thereby generating factor valuedata indicating a degree to which the factors contribute to enhancementof a fitting degree with respect to the plurality of models andrecording the factor value data in the factor value recording part.

The inner-model factor value extracting part detects a transition of thefactor data corresponding to the explanatory variable included in theregression equation of the model regarding one phenomenon and atransition of the evaluation data, thereby comparing the factorcorresponding to the explanatory variable added or deleted with respectto the regression equation with the evaluation data before and after theaddition or the deletion. Therefore, the inner-model factor valueextracting part can obtain the contribution degree to the model of thefactor corresponding to the explanatory variable added to the model ordeleted therefrom.

Furthermore, the inter-model factor value extracting part can detect anexplanatory variable (i.e., a factor) included in a plurality of modelscommonly, by referring to the factor data indicating the factorcorresponding to the explanatory variable in the plurality of models andthe evaluation data. More specifically, the factor influencing thephenomena of a plurality of models commonly is detected. Therefore, theinter-model factor value extracting part can obtain factor value dataindicating the contribution degree of a factor contributing to theenhancement of a fitting degree of the plurality of models.

Thus, the inner-model factor value extracting part calculates the valueof the factor obtained based on the transition of a factor in respectivemodels and evaluation data, and the inter-model factor value extractingpart calculates the value of the factor obtained based on theinformation on the factor in a plurality of models. Consequently, theinner-model factor value extracting part and the inter-model factorvalue extracting part determine the value of the factor from variouspoints of view. Therefore, data indicating a more general value of thefactor is obtained.

In an embodiment of the present invention, it is preferred that theinner-model factor value extracting part that, regarding the factorcorresponding to the explanatory variable added or deleted with respectto the regression equation of the model in the detected transition ofthe factor data, detects a change in a fitting degree of the modelbefore and after the addition or the deletion from the evaluation dataaccumulated in the model recording part and generates factor value dataon the factor corresponding to the explanatory variable based on adegree of the change.

According to the above configuration, in the case where an explanatoryvariable (i.e., a factor) is added or deleted with respect to aregression equation of a model, the inner-model factor value extractingpart can generate factor value data of the factor in accordance with thechange degree of a fitting degree of the model caused by the addition ordeletion of the factor. Therefore, the inner-model factor valueextracting part can generate factor value data information thatindicates how the addition or the deletion of the factor in the modelinfluences the fitting degree.

In an embodiment of the present invention, it is preferred that themodel managing part further acquires application period data indicatingan application period of the model and significance data indicatingsignificance of each factor corresponding to each explanatory variableincluded in the regression equation of the model and accumulates theapplication period data and the significance data in the model recordingpart, and the inner-model factor value extracting part detects a changein the application period of the model based on the application perioddata accumulated in the model recording part regarding at least onephenomenon, extracts a factor that contributes to enhancement of afitting degree of the model when the application period changes based ona change in the fitting degree of the model and a change in thesignificance data on each factor in the model before and after thechange in the application period, and generates factor value dataindicating a degree to which the extracted factor contributes to themodel.

According to the above configuration, in the case where the applicationperiod of a model changes with respect to a phenomenon, the inner-modelfactor value extracting part can extract a factor contributing to theenhancement of a fitting degree of the model along with the change inthe application period, in accordance with the change degree of afitting degree of the model and the change degree of significance ofeach factor. Then, factor value data on the extracted factor isgenerated. Therefore, the inner-model factor value extracting part cangenerate factor value data information indicting to which degree thefactor of the model contributes to the fitting degree along with thechange in the application period.

In an embodiment of the present invention, the model managing partfurther acquires significance data indicating significance of eachfactor corresponding to each explanatory variable included in theregression equation of the model and accumulates the significance datain the model recording part, and the inter-model factor value extractingpart refers to factor data indicating a group of factors correspondingto a group of explanatory variables included in each regression equationof a plurality of models targeting a particular phenomenon andsignificance data indicating each of the group of factors in the dataaccumulated in the model recording part, thereby generating factor valuedata indicating a contribution degree of factors that influence theplurality of models commonly.

According to the above configuration, the inter-model factor valueextracting part can generate factor value data information thatindicates commoness or uncommoness of a contributing factor in aplurality of models, such as whether a certain factor contributes to theenhancement of a fitting degree of a plurality of models commonly or thecertain factor contributes to only a part of the model.

In an embodiment of the present invention, the model managing partfurther acquires application period data indicating an applicationperiod of the model and significance data indicating significance of thefactor corresponding to the explanatory variable in the regressionequation of the model and accumulates the application period data andthe significance data in the model recording part, and the inter-modelfactor value extracting part refers to factor data indicating a group offactors corresponding to a group of explanatory variables in eachregression equation of a plurality of models having differentapplication periods, which target the same phenomenon, and significancedata indicating each of the group of factors in the data accumulated inthe model recording part, thereby generating factor value dataindicating a contribution degree of the factors with respect to theplurality of models having the different application periods.

The inter-model factor value extracting part refers to a group offactors included in each of a plurality of models targeting the samephenomenon but having different application periods and the significanceof each of the group of factors, thereby generating factor value datainformation indicating how a factor contributes to the enhancement of afitting degree over a plurality of application periods. For example,information indicating whether or not the factor included in a modelcontributes to the enhancement of a fitting degree stably over a longperiod of time, i.e., information indicating long-term stability orinstability, can be incorporated in the factor value data.

In an embodiment of the present invention, it is preferred that thecondition acquiring part acquires a regression equation of an existingmodel that is being used or is to be used in the information processingapparatus and information specifying a target phenomenon of the existingmodel from the information processing apparatus as the model conditiondata, and the model proposing part acquires factor value data associatedwith phenomenon data indicating a phenomenon that is the same as orsimilar to the target phenomenon of the existing model among the factorsindicated by the factor value data recorded in the factor valuerecording part, thereby extracting a recommended factor contributing toenhancement of a fitting degree of the existing model, creating a modelof a regression equation including an explanatory variable correspondingto the extracted recommended factor, including the created model in thesupport data as a replacement model of the existing model, andoutputting the created model to the information processing apparatus.

Thus, the model creation support system can create a placement modelwith high reasonability, which enhances the fitting degree of theexisting model of the information processing apparatus.

In an embodiment of the present invention, the model proposing partcompares factor value data on a reference factor with factor value dataon a plurality of factors other than the reference factor, using afactor corresponding to an explanatory variable included in theregression equation of the existing model as the reference factor,thereby calculating a similarity of a degree of contribution to theenhancement of a fitting degree between the reference factor and each ofthe other plurality of factors, and extracting a recommended factorcapable of contributing to the enhancement of a fitting degree of theexisting model to create the replacement model, based on the similarity.

According to the above configuration, the model proposing part canextract a factor having a factor value close to that of factors of theexisting model and include the extracted factor in factors of thereplacement model. Therefore, a replacement model including arecommended factor capable of contributing to the enhancement of afitting degree of the existing model can be created.

In an embodiment of the present invention, the condition acquiring partacquires a regression equation of an existing model that is being usedor is to be used in the information processing apparatus and informationspecifying a target phenomenon of the existing model from theinformation processing apparatus as the model condition data, and themodel proposing part extracts a plurality of replacement modelcandidates to be candidates of the replacement model, calculates asimilarity between the replacement model candidates and the existingmodel, and extracts the replacement model candidate having a relativelyhigh similarity as a replacement model.

According to the above configuration, a replacement model can be createdconsidering the similarity between models, as well as the similaritybetween factor values. Therefore, a replacement model with highreasonability, which enhances the fitting degree of the existing modeland is likely to fit for the existing model, is extracted.

In an embodiment of the present invention, the model managing partaccumulates, regarding a model represented by a regression equation inwhich an explanatory variable is a characteristics factor valuerepresenting characteristics of a factor by a vector or a matrix using 0or 1 as an element, an identifier of a model, phenomenon data indicatinga target phenomenon of the model, a regression equation of the model,factor data indicating a factor corresponding to an explanatory variableincluded in the regression equation, and evaluation data containing afitting degree of the model in the model recording part, the systemfurther including: an event information recording part that recordsevent information indicating characteristics of an event; and an eventfactor creating part that matches the event information with thecharacteristics factor value that is an explanatory variable in theregression equation of the model recorded in the model recording part,generates factor data on an event factor indicating the characteristicsof the event based on the characteristics factor value if there is thecharacteristics factor value corresponding to the event, and records thefactor data in the model recording part.

The event factor creating part creates factor data on an event factorcorresponding to event information, and records the factor data in themodel recording part. Therefore, the factor value extracting part cangenerate factor value data on an event factor and record it in thefactor value recording part, even with reference to the factor data onthe event fator. Therefore, the model proposing part generates andoutputs support data, also with reference to the factor value data onthe event factor in the factor value recording part. Thus, a model alsoconsidering event information can be created.

In an embodiment of the present invention, the model managing partaccumulates, regarding a model represented by a regression equation inwhich an explanatory variable is a time factor value representing timecharacteristics of a factor by a vector or a matrix using 0 or 1 as anelement, an identifier of a model, phenomenon data indicating a targetphenomenon of the model, a regression equation of the model, factor datacontaining a time factor value of an explanatory variable included inthe regression equation, and evaluation data containing a fitting degreeof the model in the model recording part, the system further including afactor procuring part that acquires designated factor data indicating adesignated factor requested to be modified from the informationprocessing apparatus or the model proposing part, matches a time factorvalue of the designated factor with a time factor value of the factordata in the model recording part, thereby extracting a factor having apredetermined relationship with the designated factor from the modelrecording part and recording factor data represented by a time factorvalue of the extracted factor or a complex time factor value obtained byan OR or an AND of the time factor value of the extracted factor and atime factor of the designated factor in the model recording part asfactor data on a modified factor of the designated factor.

According to the above configuration, the factor procuring part canautomatically extract a combination of effective time factors from thegroup of time factors of a model recorded in a model recording part, andnewly record the combination in a factor value recording part as acomplex time factor.

An embodiment of the present invention may be a model creation supportprogram causing a computer to perform processing, which is capable ofaccessing an information processing apparatus that predicts or analyzesa phenomenon to be a target for prediction or analysis, using a modelthat is data indicating the phenomenon as an objective variable in aregression equation utilizing an explanatory variable corresponding to afactor contributing to the phenomenon. The model creation supportprogram causes the computer to perform the following proceedings: modelmanaging processing of acquiring an identifier of the model, phenomenondata indicating the phenomenon to be a target of the model, theregression equation of the model, factor data indicating the factorcorresponding to the explanatory variable included in the regressionequation, and evaluation data containing a fitting degree of the modelfrom the information processing apparatus, and accumulating them in amodel recording part accessible from the computer; factor valueextracting processing of, regarding at least one model, referring to thefactor data corresponding to the explanatory variable in the regressionequation of the model and the evaluation data on the model in the dataaccumulated in the model recording part, thereby generating factor valuedata indicating a degree to which the factor indicated by the factordata contributes to enhancement of the fitting degree of the model andrecording the factor value data in a factor value recording partaccessible from the computer so that the factor value data is associatedwith the phenomenon data indicating the phenomenon to be a target of themodel; condition acquiring processing of receiving a request forsupporting model creation from the information processing apparatus, andfurther receiving an input of model condition data containing dataindicating a phenomenon to be a target of a requested model; and modelproposing processing that matches the data indicating the phenomenon tobe a target of the requested model contained in the model condition datawith the phenomenon data associated with the factor value data in thefactor value recording part, thereby extracting a recommended factorcapable of contributing to the enhancement of a fitting degree of therequested model and outputting support data containing data indicatingthe extracted recommended factor to the information processingapparatus.

An embodiment of the present invention may be a model creation supportmethod performed by a computer capable of accessing an informationprocessing apparatus that predicts or analyzes a phenomenon to be atarget for prediction or analysis, using a model that is data indicatingthe phenomenon as an objective variable in a regression equationutilizing an explanatory variable corresponding to a factor contributingto the phenomenon. The method includes: acquiring an identifier of themodel, the regression equation of the model, factor data indicating thefactor corresponding to the explanatory variable included in theregression equation, and evaluation data containing a fitting degree ofthe model from the information processing apparatus, and accumulatingthem in a model recording part accessible from the computer; regardingat least one model, referring to the factor data corresponding to theexplanatory variable in the regression equation of the model and theevaluation data on the model in the data accumulated in the modelrecording part, thereby generating factor value data indicating a degreeto which the factor indicated by the factor data contributes toenhancement of the fitting degree of the model and recording the factorvalue data in a factor value recording part accessible from the computerso that the factor value data is associated with the phenomenon dataindicating the phenomenon to be a target of the model; receiving arequest for supporting model creation from the information processingapparatus, and further receiving an input of model condition datacontaining data indicating a phenomenon to be a target of a requestedmodel; and matching the data indicating the phenomenon to be a target ofthe requested model indicated by the model condition data with thephenomenon data associated with the factor value data in the factorvalue recording part, thereby extracting a recommended factor capable ofcontributing to the enhancement of a fitting degree of the requestedmodel and outputting support data containing data indicating theextracted recommended factor to the information processing apparatus.

Embodiment 1

FIG. 1 is a functional block diagram showing a configuration of a modelcreation support system according to the present embodiment. A modelcreation support system 1 shown in FIG. 1 is connected to informationprocessing apparatuses 15 a, 15 b, and 15 c. The information processingapparatuses 15 a to 15 c respectively analyze data indicating a certainphenomenon and create a model, and predict a future phenomenon, usingthe model. The model is data indicating a regression equation utilizingexplanatory variables corresponding to factors contributing to aphenomenon to be a target. The information processing apparatus 15 aincludes a model creation updating part 151 that creates or updates amodel, and a model evaluating part 152 that generates evaluation data onthe model.

The model creation support system 1 supports the creation and update ofa model to be used in the respective information processing apparatuses15 a to 15 c. The schematic operation of the model creation supportsystem 1 is to collect and record information on models from therespective information processing apparatuses 15 a to 15 c, generatesupport data for creating models useful respectively in the informationprocessing apparatuses 15 a to 15 c, using the information, and outputthe support data to the respective information processing apparatuses 15a to 15 c.

For the above purpose, the model creation support system 1 includes anIF part 2, a model information acquiring part 3, a condition acquiringpart 4, a model managing part 5, a value extracting part 7, a distancecalculating part 8, a space creating part 9, a model proposing part 11,a model recording part 6 a, and a factor value recording part 6 b.Hereinafter, each functional part of the model creation support system 1and the information processing apparatus 15 a will be described.

(Specific Example of an Information Processing Apparatus)

First, a specific example of the information processing apparatus 15 awill be described. Herein, as an example, the case will be describedwhere the information processing apparatus 15 a creates a modeltargeting the number of system troubles in a certain financialinstitution. In this case, the model creation updating part 151 of theinformation processing apparatus 15 a generates a regression equation bysubjecting time-series data on the number of system troubles to aregression analysis. An example of the regression equation isrepresented by the following Expression (2).Y=β ₀ ·X ₀+β₁ ·X ₁+β₂ ·X ₂+β₃ ·X ₃  (2)

In the above Expression (2), Y is an objective variable representing thenumber of system troubles. Y is expressed by a vector, for example,using the number of system troubles per day as each element. Forexample, Y representing the transition of the number of system troublesfor one year (=365 days) from Jan. 1, 2007 is a matrix with one columnand 365 rows (365-dimensional vector).

X₀ is a constant term. X₁ to X₃ are explanatory variables respectivelycorresponding to three factors contributing to the number of systemtroubles. β₀ to β₃ represent weights (parameters) of X₀ to X₃. Herein,as an example, it is assumed that the explanatory variables X₁, X₂, andX₃ correspond to “beginning of next week” “rainy season” and “Wednesday,Thursday, Friday in winter” respectively. Factors expressed by dayspecies that mean days having particular attributes, such as “beginningof next week” “rainy season” and “Wednesday, Thursday, Friday in winter”are referred to as day factors. The day factors can be expressed by avector or a matrix, for example, in which an element corresponding to anapplicable day is 1 and an element corresponding to a day that is notapplicable is 0. For example, the explanatory variable X₁ correspondingto the day factor “beginning of next week” for one year (=365 days) fromJan. 1, 2007 is expressed by a matrix with one column and 365 rows(365-dimensional vector) represented by the following Expression (3).

$\begin{matrix}{X_{0} = \begin{pmatrix}1 \\1 \\0 \\0 \\0 \\\vdots \\0\end{pmatrix}} & (3)\end{matrix}$

The explanatory variable is not limited to a day factor. For example, amatrix or a vector using 0 or 1 as an element, representing acharacteristics factor that is characteristics characterized by a place,an event, or the like, may be used as an explanatory variable. Thus, byrepresenting a factor by an explanatory variable of a matrix or a vectorusing 0 or 1 as an element, one factor is likely to be used in aplurality of different models, and the factor is likely to be re-used.The explanatory variable is not limited to such a matrix or a vector,and can be expressed arbitrarily.

Thus, for example, in order to create a regression expression of thenumber of system troubles for one year, it is necessary to determine theexplanatory variables X₁ to X₃. The explanatory variables X₁ to X₃ areselected by a user. More specifically, a user inputs data indicatingfactors (for example, “beginning of next weak”, “rainy season” and“Wednesday, Thursday, Friday in winter”) influencing the number ofsystem troubles. The model creation updating part 151 calculatesnumerical values (matrix) of the explanatory variables X₁ to X₃ for oneyear from Jan. 1, 2007, for example, from the factors input by the user.Furthermore, the model creation updating part 151 performs a regressionanalysis using the numerical values of the explanatory variables X₁ toX₃ and actually measured values of the number of system troubles of eachday in one year from Jan. 1, 2007, and calculates coefficients β₀ to β₂in the above Expression (2). The regression equation represented byExpression (2) obtained from the calculation becomes a model of thenumber of system troubles.

The model evaluating part 152 calculates a fitting degree of the modelcreated by the model creation updating part 151, and records the fittingdegree in the information processing apparatus 15 a as evaluation data.The fitting degree includes the following two kinds. One of them showsthe goodness of fit of a predicted value calculated by the model withrespect to an actually measured value used for creating the model. Theother one shows to which degree a predicted value obtained by predictinga future using the model can fit a resultant value representing aphenomenon that has actually occurred. In the following, the former willbe referred to as a “fitting degree of analysis-estimation” (fittingdegree, focused on current data explanation) and the latter is referredto as a “fitting degree of prediction result” (fitting degree, focusedon future data prediction). Model estimation is usually obtaining amodel which most explains the current data (the actually measuredvalue). This estimation is referred to as “estimation of analysis” inthe present specification. On the other hand, the model for predictingthe data variations of the future may be estimated. This estimation isreferred to as “estimation of prediction ” in the present specification.In estimation of prediction, even if deviation arises to the model, abrief model often obtains a correct prediction distribution.

For example, the fitting degree of analysis-estimation shows to whichdegree a predicted value Y₂₀₀₇ obtained by calculating the number ofsystem troubles for one year from Jan. 1, 2007 in accordance with theabove Expression (2) fits an actually measured value of the number ofsystem troubles for one year from Jan. 1, 2007. On the other hand, forexample, it is assumed that the number of system troubles Y₂₀₀₈ for oneyear from Jan. 1, 2008 is calculated in December 2007 in accordance withthe above Expression (2). After that, an actually measured value of thenumber of system troubles from Jan. 1, 2008 to Dec. 31, 2008 is obtainedat the end of Dec. 31, 2008 after the passage of time. The fittingdegree of prediction result shows to which degree the predicted valueY₂₀₀₈ fits the actually measured value of the year 2008.

In the following, a period covered by an actually measured value usedduring the creation of a model such as one year from Jan. 1, 2007 willbe referred to as an “analysis application period” and a period targetedfor prediction such as a period from Jan. 1, 2008 to Dec. 31, 2008 willbe referred to as a “prediction application period”. The applicationperiod includes both the analysis application period and the predictionapplication period.

As a typical example of a method for calculating a fitting degree ofanalysis-estimation of a model, the square of a coefficient ofdetermination in a regression equation can be calculated as a numericalvalue of a fitting degree of analysis-estimation. Regarding the fittingdegree of prediction result, for example, an average absolute errorratio representing an absolute value ratio of a predicted error withrespect to an actually measured value is calculated, and a numericalvalue obtained by subtracting the average absolute error ratio from 1(referred to as an average explanatory ratio) can be used as a numericalvalue representing a fitting degree of prediction result. Regarding thedegree to which a variable contributes to the improvement of a model,the t-test value of the variable can be considered as a typical index atan analysis time, and an average explanatory ratio can be considered asa typical index at a prediction time. Method for calculating an analysisprediction and a fitting degree of prediction result are not limited tothe above examples.

The information processing apparatus 15 a calculates a predicted valueof a future phenomenon (for example, the number of system troubles forthe next one year), using the model created by the model creationupdating part 151. The predicted value is referred to by a usermaintaining a system of a financial institution and used for business.Furthermore, the user can cause the model creation updating part 151 toupdate the model so that the model fits an actual phenomenon that islikely to change daily. For example, the user inputs a factor desired tobe added to the model, and the model creation updating part 151 cancreate a regression equation with an explanatory variable correspondingto the input factor added thereto and update the model using theregression analysis calculation.

The information processing apparatuses 15 b and 15c can also beconfigured in the same way, respectively. However, the configurations ofthe information processing apparatuses 15 a to 15c are not limited tothat of the information processing apparatus 15 a, and only need to havea function of creating and updating a model and a function of generatingevaluation data on the model. In FIG. 1, although three informationprocessing apparatuses are connected to the model creation supportsystem 1, more information processing apparatuses may be connectedthereto.

(Explanation of Each Functional Part of the Model Creation SupportSystem 1)

Next, each functional part of the model creation support system 1 willbe described. The IF part 2 enables the communication between the modelcreation support system 1 and the information processing apparatuses 15a to 15 c. Herein, the connection form between the model creationsupport system 1 and the information processing apparatuses 15 a to 15cis not particularly limited, and the model creation support system 1 andthe information processing apparatuses 15 a to 15c may be connected, forexample, by cable or wireless. The model creation support system 1 andthe information processing apparatuses 15 a to 15c may also be connectedover the Internet or an intranet.

The model information acquiring part 3 requests model information withrespect to the respective information processing apparatuses 15 a to 15cvia the IF part 2, and receive model information therefrom. The receivedmodel information is passed to the model managing part 5. The modelinformation contains, for example, data indicating a regression equationof a model, data indicating a target phenomenon of the model andfactors, evaluation data on the model, data indicating an analysisapplication period and a prediction application period of the model, andthe like. The evaluation data on the model contains, for example, dataindicating the fitting degree of a model (at least one of a fittingdegree of analysis-estimation and a fitting degree of predictionresult), and the significance of each factor of the model.

The model information acquiring part 3 may, for example, request andreceive model information periodically. Alternatively, in each of theinformation processing apparatuses 15 a to 15 c, when a model is createdor updated, model information may be sent to the model informationacquiring part 3 automatically together with an update notification or anew creation notification.

The model managing part 5 records the model information acquired by themodel information acquiring part 3 in the model recording part 6 a. Inthe model recording part 6 a, as an example, an objective variable DB61, an explanatory variable DB 62, and a model instance DB 63 areconstructed. Among the model information, for example, the modelmanaging part 5 records data indicating a regression equation, dataindicating an analysis application period and a prediction applicationperiod, and evaluation data in the model instance DB 63, records dataindicating the phenomenon of the model in the objective variable DB 61,and records data indicating the factors of the model in the explanatoryvariable DB 62.

FIG. 2 shows an exemplary structure of data to be recorded in the modelinstance DB 63. In the example shown in FIG. 2, a phenomenon, anobjective variable ID, an explanatory variable ID, a model (regressionequation), an analysis application period, a prediction applicationperiod, a fitting degree of analysis-estimation, a fitting degree ofprediction result, and the significance of each factor are recorded soas to be associated with an instance ID as one record.

In the case where a model form in the information processing apparatuses15 a to 15 c is changed, for example, as in the case where a part offactors of a model is changed and the case where an analysis applicationperiod or a prediction application period is changed in the informationprocessing apparatuses 15 a to 15 c, the model managing part 5 can newlyassign an instance ID to the changed model form, and record the changedmodel form in the model instance DB 63 as a new record. Thus, the modelform changing in accordance with the situation at a spot can bereflected to the model instance DB 63.

FIG. 3 is a diagram showing an exemplary structure of data to berecorded in the objective variable DB 61. In the example shown in FIG.3, a phenomenon represented by an objective variable, the place of thephenomenon, the kind of the phenomenon, a category (region), a category(field), and a category (institution) are recorded so as to beassociated with the objective variable ID as one record.

FIG. 4 is a diagram showing an exemplary structure of data to berecorded in the explanatory variable DB 62. In the example shown in FIG.4, the explanatory variable DB 62 contains factor tables storing recordsC1 and C2, and an element value table storing a record C100. In each ofthe records C1 and C2, category information such as a factor name and acoordinate space (for example, a calendar, a map) to which factorscommonly belong is recorded so as to be associated with an explanatoryvariable ID as one record.

For example, as shown in the above Expression (3), in the case where theexplanatory variable of a day factor is expressed by a matrix with onecolumn and 365 rows, using each of 365 days of one year as an element, acoordinate space to which factors commonly belong is “one year=365days”. Thus, by defining a coordinate space to which day factorscommonly belong, for example, a similarity between day factors and thelike can be calculated. A coordinate space to which factors commonlybelong is not limited to a temporal space as in “one year=365 days”, andfor example, may be a geometrical space such as a Japanese map.Furthermore, in the record C100 of the element value table, factor namesshowing day species such as “Monday”, “Tuesday”, “Wednesday”,“Holiday/Substitute holiday”, “Beginning of midwinter”, “Month days witha multiple of five of a month” and “January” are recorded on theuppermost row. In each column, day factor values (element values)showing days specified by each day species of the factor names arerecorded. The day factor values are discrete values recorded for eachday from January 1 to December 31 in a row direction, and “1” isrecorded on days corresponding to the day species and “0” is recorded onthe other days.

The model managing part 5 further instructs the value extracting part 7to extract values of factors of a model recorded in the model instanceDB 63. The value extracting part 7 refers to the data in the objectivevariable DB 61, the explanatory variable DB 62, and the model instanceDB 63 in the model recording part 61, calculates the values of factorscontained in each model, and records the values in the factor valuerecording part 6 b.

The value of a factor is expressed by a value attribute and a numericalvalue showing a value degree. The value attribute can be defined, forexample, by various natures of a factor obtained during the change of amodel targeting one phenomenon or from the comparison between aplurality of models. Examples of the value attribute include a fittingdegree enhancement property by a factor change, a fitting degreeenhancement property by an application period change, long-termstability, and commoness. By defining a factor value attribute asdescribed above, a factor value can be evaluated from a plurality ofpoints of view, and appropriate evaluation can be made. The factor valueattribute is not limited to the above example.

The fitting degree enhancement property by a factor change refers to thedegree to which a factor of a model enhances the fitting degree of themodel by being added to or deleted from the model. The fitting degreeenhancement property by an application period change refers to thedegree to which a factor of a model enhances the fitting degree of themodel at a time of change in an analysis application period orprediction application period of the model.

The long-term stability refers to the stability of the degree ofinfluence which a factor has on a model showing a phenomenon for a longperiod of time with the passage of a time. The commoness shows to whichdegree one factor is used commonly to a plurality of models, andcontributes to a fitting degree.

The value extracting part 7 includes an inner-model factor valueextracting part 71 and an inter-model factor value extracting part 72.The inner-model factor value extracting part 71 extracts a model formchange such as a change in factors of a model and a change in ananalysis application period or a prediction application period withreference to the model instance DB 63. Then, the inner-model factorvalue extracting part 71 compares evaluation data on models before andafter the change, thereby extracting a factor contributing to theenhancement of a fitting degree and calculating the value of the factor(a specific example of a calculation method will be described later).Thus, the magnitude of a factor value regarding the enhancement of afitting degree involved in a model form change, such as a fitting degreeenhancement property by a factor change and a fitting degree enhancementproperty by an application period change, can be calculated.

The inner-model factor value extracting part 71 records the dataindicating the magnitude of a factor value obtained by the calculation,and the data indicating information for identifying a factor, the valueattribute of the factor, and a phenomenon to be a target of a model inwhich the factor contributes to the enhancement of a fitting degree sothat these data are associated with each other in a factor value DB 65.

FIG. 5 is a diagram showing an exemplary structure of data to berecorded in the factor value DB 65. A record D1 shown in FIG. 5 is anexemplary record showing a value (fitting degree enhancement property bya factor change) of a factor “late June to mid-July”. A record D2 is anexemplary record showing a value (fitting degree enhancement property byan application period change) of a factor “rainy season”. The records D1and D2 are data in which a contribution factor (explanatory variableID), a value attribute, an objective variable ID, a category of anobjective variable (field), an instance ID of a model before a change,an instance ID of a model after a change, and an analysis applicationperiod, and enhancement performance are recorded so as to be associatedwith each other. The objective variable ID shows an objective variableof a model before a change and a model after the change. The objectivevariable ID is an example of data indicating a phenomenon to be a targetof a model in which a factor contributes to the enhancement of a fittingdegree.

The inter-model factor value extracting part 72 refers to a plurality ofrecords in the model instance DB 63, and acquires an explanatoryvariable ID and evaluation data in each record. Then, the inter-modelfactor value extracting part 72 extracts a factor (explanatory variable)that contributes to the enhancement of a fitting degree with respect toa plurality of models represented by a plurality of records based on theacquired information, and calculates the contribution degree thereof.Thus, for example, the magnitude of a factor value with respect to aplurality of models, such as long-term stability and commoness, can becalculated. Data indicating the magnitude of a factor value is recordedin the factor value DB 65 together with factor information.“Contribution Degree” explains how each parameter contributes to themodel estimation or to the prediction of future variations. In the caseof analysis, representative measurement of a contribution degree of aparameter is, for example, a t-test value of that parameter. On theother hand, in the case of prediction, representative measurement of acontribution degree of a parameter is, for example, an average absoluteerror ratio.

A record D3 shown in FIG. 5 is an exemplary record showing each factorvalue (long-term stability) corresponding to explanatory variable IDs“s-031, s-101, s-017”. The record D3 is data in which a contributionfactor (explanatory variable ID), a value attribute, an objectivevariable ID, a category of an objective variable (field), an instance IDof a typical model, a corresponding period, and a fitting degree arerecorded so as to be associated with each other. A record D4 is anexemplary record showing respective factor values (commoness)corresponding to explanatory variable IDs “s-031, s-101”. The record D4is data in which a contribution factor (explanatory variable ID), avalue attribute, an objective variable ID, a category of an objectivevariable (field), an instance ID of a typical model, the number ofadopted spots, a field adoption ranking, and a model average fittingdegree are recorded so as to be associated with each other.

As described above, the model information acquiring part 3 and the modelmanaging part 5 collect information on models created respectively bythe information processing apparatuses 15 a to 15 c and accumulate theinformation in the model recording part 6 a. The value extracting part 7generates data indicating model factor values from the accumulated modelinformation and records the data in the factor value recording part 6 b.These data are used by the model proposing part 11, the distancecalculating part 8, and the space creating part 9 described next. Morespecifically, as in the above example, the factor values are classifiedinto a plurality of attributes from various points of views and recordedin the factor value DB 65, whereby the calculation of a similaritybetween factor values described later and the like can be performed.

Next, the condition acquiring part 4, the model proposing part 11, thespace creating part 9, and the distance calculating part 8 will bedescribed. The condition acquiring part 4 receives a request forsupporting model creation from the information processing apparatuses 15a to 15 c via the IF part 2. Furthermore, the condition acquiring part 4not only requests model creation support but also receives modelcondition data indicating the condition of a requested mode. The modelcondition data contains at least data indicating a target phenomenon ofthe requested model. The data indicating this phenomenon indicates, forexample, an objective variable in a regression equation of the requestedmodel.

The request for supporting model creation received by the conditionacquiring part 4 may be a request for creating a replacement model or areplacement factor obtained by changing an existing model created by aninformation processing apparatus of a request origin so as to enhance afitting degree, or a request for creating a new model. In the case ofthe former, the model condition data contains, for example, informationindicating the existing model created by the information processingapparatus of a request origin, information indicating factors desired tobe retained or factors desired to be changed among factors of theexisting model. The condition acquiring part 4 can acquire such modelcondition data simultaneously with a model creation request, or canacquire such model condition data by requesting the model condition datawith respect to the information processing apparatus of a request originafter receiving a model creation request.

The condition acquiring part 4 passes the model condition data to themodel proposing part 11. The model proposing part 11 generates a modelin accordance with the condition indicated by the model condition data,and outputs the model to the information processing apparatus.Specifically, the model proposing part 11 determines explanatoryvariables to be included in a regression equation of the requested modelin accordance with the condition. At that time, the model proposing part11 extracts factors that can contribute to the enhancement of a fittingdegree of the requested model with reference to the factor value DB 65.Since the model condition data contains data indicating a targetphenomenon of the requested mode, the model proposing part 11 canacquire information on factors that can contribute to the enhancement ofa fitting degree of the requested model by searching for factor valuedata associated with the phenomenon.

Furthermore, the model proposing part 11 requests the space creatingpart 9 to collect information so as to search for information on desiredfactors or a desired model in the model recording part 6 a and thefactor value recording part 6 b. The space creating part 9 includes asimilar factor space creating part 91 and a similar model space creatingpart 92.

The similar factor space creating part 91 creates similar factor spacedata containing information on a similar factor having a value similarto a value of a factor to be a reference (hereinafter, referred to as areference factor), and information indicating the relationship betweenthe similar factor and the reference factor. An example of the similarfactor space data includes data in which data specifying the similarfactor and data indicating the distance (similarity) between a factorvalue of the similar factor and a factor value of the reference factorare recorded so as to be associated with each other.

At a time of creating similar factor space data, the similar factorspace creating part 91 requests the distance calculating part 8 tocalculate the distance (similarity) between the factor values. Thedistance calculating part 8 acquires the factor value data on twofactors targeted for calculation, and calculates the distance betweenthe two factor values based on the factor value data.

The distance between the factor values can be expressed by numericalvalues respectively, for example, with respect to a plurality of pointsof view such as the similarity of factor value attributes, thesimilarity of the contribution degrees of factors with respect to amodel, the similarity of models to which the factors belong, and thesimilarity of factors themselves. These numerical values are substitutedinto a predetermined mathematical expression, whereby the distancebetween the factor values can be calculated.

Although the mathematical expression is not particularly limited, it ispreferred that an expression reflects the similarity of a plurality ofpoints of view totally. For example, a numerical expression ispreferably as follows: when only the similarity of one point of viewincreases with the similarity of the other points of view remaining asit is, the distance between factor values to be calculated also changesin accordance with the degree of the increase.

The similarity model space creating part 92 creates similar model spacedata indicating a virtual space in which similar models similar to amodel to be a reference (hereinafter, referred to as a reference model)are placed around the reference model in accordance with the similarity(distance) with respect to the reference model. An example of thesimilar model space data includes data in which data specifying asimilar model and the distance between the similar model and thereference model are recorded so as to be associated with each other.

At a time of creating model space data, the similar model space creatingpart 92 requests the distance calculating part 8 to calculate thesimilarity (distance) between models. The distance calculating part 8acquires data on two models to be calculated from the model instance DB63, the objective variable DB 61, and the explanatory variable DB 62,and calculates the distance between the two models based on the data.

The distance between the models can be digitized from a plurality ofpoints of view such as the similarity of target phenomena of the models(similarity between objective variables in regression equations), thesimilarity between factors included in the models (similarity betweenexplanatory variables), and the like. These numerical values aresubstituted into a predetermined mathematical expression, whereby thedistance between the models can be calculated. Although the mathematicalexpression is not particularly limited, an expression preferablyreflects the similarity of a plurality of points of view totally.

The model proposing part 11 requests the similarity factor spacecreating part 91 or the similarity model space creating part 92 toperform processing in accordance with the model creation request and themodel condition data. The model proposing part 11 creates support dataoutput to the information processing apparatus, based on the similarfactor space data and the similar model space data obtained as a result.

The model creation support system 1 is constructed on a computer such asa server machine, a personal computer, or a work station. The respectivefunctional parts of the IF part 2, the model information acquiring part3, the condition acquiring part 4, the model managing part 5, the valueextracting part 7, the distance calculating part 8, the space creatingpart 9, and the model proposing part 11 of the model creation supportsystem 1 may be configured on one computer or may be distributed into aplurality of computers. Furthermore, the respective functional parts arerealized when the CPU of a computer executes a predetermined program.Thus, a program for executing each of the functions and a recordingmedium storing the program are also included in one embodiment of thepresent invention. Furthermore, the model recording part 6 a and thefactor value recording part 6 b are embodied by a recording medium suchas a memory or a hard disk provided in a computer.

(Operation Example of the Model Creation Support System)

The processing executed by the model creation support system 1 mainlyincludes accumulation processing of accumulating model information andfactor value information and model creation processing. The accumulationprocessing is mainly executed by the model information acquiring part 3,the model managing part 5, and the value extracting part 7. The modelcreation processing is mainly executed by the condition acquiring part4, the model proposing part 11, the distance calculating part 8, and thespace creating part 9. In the following, the outline of the accumulationprocessing and the model creation procession will be described, andthereafter, each processing will be described in detail.

(Outline of Accumulation Processing)

In the present embodiment, the model information acquiring part 3requests model information with respect to the information processingapparatuses 15 a, 15 b, and 15 c at a constant period. When receiving arequest for model information, the information processing apparatuses 15a, 15 b, and 15 c return information on newly created models or updatedmodels after the previous request to the model information acquiringpart 3.

The acquisition timing of information regarding the models is notlimited thereto. The model information acquiring part 3 may receiveinformation sequentially, for example, every time a model is newlycreated or updated in the information processing apparatus 15 a, 15 b,or 15 c.

In the case where the information processing apparatus 15 a, 15 b, or 15c newly creates a model, the model information acquiring part 3receives, for example, data indicating a regression equation of themodel, data indicating a target phenomenon and factors of the model,evaluation data on the model, and data indicating an analysisapplication period and a prediction application period of the model.

In the case where the information processing apparatus 15 a, 15 b, or 15c updates a model, the model information acquiring part 3 receives theupdated contents. The update of the model includes, for example, thechange in factors of the model, the change in an application period, andthe change in evaluation data. In the present embodiment, the modelinformation acquiring part 3 receives a regression equation andevaluation data before and after a change in the case of the change infactors, an application period and evaluation data before and after achange in the case of the change in an application period, andevaluation data after a change in the case of the change in evaluationdata.

The model information acquiring part 3 notifies the model managing part5 of a request for storing model information together with modelinformation, for example, in the case where a model is newly created orupdated in the information processing apparatus 15 a, 15 b, or 15 c. Themodel managing part 5 executes processing based on the receivedinformation.

FIG. 6 is a flowchart illustrating an operation example of the modelmanaging part 5. When receiving a request for storing model informationfrom the model information acquiring part 3 (Y in Op1), the modelmanaging part 5 receives information to be stored from the modelinformation acquiring part 3 and records the information in the modelrecording part 6 a (Op2). Hereinafter, the processing in Op2 in threecases: the case where a model is newly created; the case where a modelfactor or an application period is changed, and the case whereevaluation data on the model (for example, a fitting degree ofprediction result, a fitting degree of analysis-estimation, or thesignificance of each factor) is updated will be described, respectively.

When a model is newly created, the model managing part 5 newly generatesan instance ID in Op2. Then, the model managing part 5 receives datasuch as a regression equation of the newly created model, a phenomenonto be predicted, the kind of the phenomenon, categories (region, field,institution) of the phenomenon, factor names, an analysis applicationperiod, a prediction application period, a fitting degree ofanalysis-estimation, and the significance of each factor, and recordsthe data in the model instance DB 63, the objective variable DB 61, andthe explanatory variable DB 62, with the instance ID associatedtherewith.

In the case of a change in factors of the model or a change in anapplication period, the model managing part 5 acquires information forspecifying a model before the change (a regression equation before thechange, a phenomenon to be predicted, etc.) and the informationregarding updated contents from the model information acquiring part 3.Then, the model managing part 5 newly generates an instance ID, andrecords the information regarding the updated contents acquired from themodel information acquiring part 3 in the model instance DB 63, with theinstance ID associated therewith.

In the case where the evaluation data (for example, a fitting degree ofprediction result, a fitting degree of analysis-estimation, or thesignificance of each factor) of the model is updated, the model managingpart 5 receives the updated evaluation data from the model informationacquiring part 3, and updates the evaluation data corresponding to theinstance ID of the model in the model instance DB 63.

The processing in Op2 is as described above. Next, when the modelinformation acquiring part 3 notifies the model managing part 5 of achange in factors of a model or a model application period (Y in Op3),the model managing part 5 instructs the inner-model factor valueextracting part 71 to extract a factor having a fitting degreeenhancement property and to record factor value data on the factor(Op4). The fitting degree enhancement property includes a fitting degreeenhancement property by a change in factors, and a fitting degreeenhancement property by an change in an application period. At a time ofthe instruction, the model managing part 5 receives regression equationsbefore and after the change or application periods before and after thechange, and evaluation data (a fitting degree, the significance of eachfactor, etc.) before and after the change, and passes them to theinner-model factor value extracting part 71. The inner-model factorvalue extracting part 71 calculates a fitting degree enhancementproperty by a change in factors or a fitting degree enhancement propertyby a change in an application period of the factor included in the modelafter the change, and records it in the factor value DB 65. The detailof the inner-model factor value extracting part 71 will be describedlater in detail.

At a time of starting a night batch (Y in Op5), the model managing part5 instructs the inter-model factor value extracting part 72 to extractfactors having long-term stability or commoness, and to record factorvalue data on the factors (Op6). Thus, factors having long-termstability and commoness are extracted periodically, and factor valuedata on the factors is recorded in the factor value DB 65. The detail ofthe inter-model factor value extracting part 72 will be described later.

The processings in Op1 to Op6 are repeated unless a stop instruction isprovided (as long as N is provided in Op7). Thus, information on a modelthat is newly created or updated in the information processingapparatuses 15 a to 15 c is reflected to the sequential model recordingpart 6 a. Furthermore, the factor value data is also updated insynchronization with the update of information to be recorded in themodel recording part 6 a.

(Outline of a Model Creation Processing)

The condition acquiring part 4 receives a model creation request andmodel condition data from the information processing apparatuses 15 a to15 c. In the present embodiment, as an example, the case where thecondition acquiring part 4 receives a request for creating a replacementmodel from the information processing apparatus 15 a will be described.In this case, the condition acquiring part 4 receives model conditiondata together with the request for creating a replacement model, andpasses the received data to the model proposing part 11.

The model proposing part 11 generates a replacement model based on thedata, and outputs the replacement model to the information processingapparatus 15 a. FIG. 7 is a flowchart illustrating an operation examplewhen the model proposing part 11 receives a request for creating areplacement model.

In Op11 in FIG. 7, the model proposing part 11 acquires the request forcreating a replacement model and the model condition data from thecondition acquiring part 4. The model condition data contains, forexample, data indicating a regression equation of an existing modelrequested to be replaced in the information processing apparatus 15 aand a replacement target range in the existing model. More specifically,the data indicating the replacement target range contains dataindicating whether the existing model is replaced on a model basis or ona factor basis. Furthermore, data specifying a factor to be replaced isalso contained in the data indicating the replacement target range.

Herein, the case where a regression equation of the existing model isexpressed by the same expression as Expression (2) will be illustrated.Y=β ₀ ·X ₀+β₁ ·X ₁+β₂ ·X ₂+β₃ ·X ₃  (2)

As an example, in the above Expression (2), it is assumed that anobjective variable Y, an explanatory variable X₁, an explanatoryvariable X₂, and an explanatory variable X₃ respectively correspond tothe number of system troubles in S branch office of A creditassociation, a factor “beginning of next weak”, “rainy season” and“Wednesday, Thursday, Friday in winter”.

In the case where the model condition data indicates that the existingmodel is replaced on a factor basis (“on a factor basis” in Op12), themodel proposing part 11 instructs the similar factor space creating part91 to create similar factor space data with a factor to be replaced as areference factor (Op13). The similar factor space data indicates avirtual space composed of a group of similar factors having a valuesimilar to that of the reference factor.

For example, it is assumed that the model condition data indicates thatthe factor to be replaced is a day factor “rainy season” correspondingto X₂. In this case, with a day factor “rainy season” as a referencefactor, similar factor space data containing a group of similar factorshaving a value similar to that of the reference factor and informationindicating the relationship between each similar factor and thereference factor is created. Then, the model proposing part 11 receivesthe similar factor space data from the similar factor space creatingpart 91. The detail of the processing of generating similar factor spacedata will be described later. In the case where there are a plurality offactors to be replaced, similar factor space data is generated for eachreference factor, with each of the plurality of factors as a referencefactor.

The similar factor space data is, for example, data in which dataspecifying a similar factor, and the distance (similarity) between thereference factor and the similar are recorded so as to be associatedwith each other. The following Table 1 shows an example of the contentsof the similar factor space data in the case where day factors “summersolstice or early summer” “early summer or midsummer” “early July”,“summer solstice”, “summer solstice or early summer” “late June tomid-July”, “July”, and “early July to mid-July” are extracted as similarfactors of the reference factor “rainy season”. In the example shown inthe following Table 1, the place of a target phenomenon of a model towhich the similar factors belong, the kind of the phenomenon, a factorvalue common to that of the reference factor, the names of the similarfactors, and distances are associated with each other.

TABLE 1 Place of a target Kind of a target Common factor value Nameofsimilar factors phenomenon phenomenon Distance Long-term stabilitySummer solstice or H branch office of B Number of system 9 early summerbank troubles in a local bank Long-term stability Early summer or Sbranch office of A Number of system 18 midsummer credit associationtroubles in a credit association Long-term stability Early July U branchoffice of C Number of system 10 bank troubles in a bank Long-termstability Summer solstice N branch office of K Number of system 23 banktroubles in an automatic teller machine Long-term stability Summersolstice or U branch office of Number of system 8 early summer H creditassociation troubles in a credit association Long-term stability LateJune to mid-July T branch office of E Number of system 15 bank troublesin a credit association Long-term stability July N branch office of SNumber of system 4 bank troubles in an automatic teller machineLong-term stability Early July to mid-July S branch office of K Numberof system 5 bank troubles in a credit association

In the example shown in Table 1, although a scalar value indicating adistance (similarity) is recorded as information on the relationshipbetween the reference factor and the similar factors, the information isnot limited thereto. For example, data indicating the similarity betweena plurality of factors calculated from a plurality of points of view asa vector or a coordinate may be recorded.

The model proposing part 11 sends the similar factor space data to theinformation processing apparatus 15 a, and causes the informationprocessing apparatus 15 a to present the similar factors to a user sothat the user can select the similar factors (Op14). In this case, it ispreferred that the similar factors are presented so that the distancefrom the reference factor (=factor of a replaced) can be grasped.

FIG. 8 is a diagram showing a screen display example of the similarfactor space data having the contents shown in Table 8 in theinformation processing apparatus 15 a. In the example shown in FIG. 8, adisplay region 22 of a factor to be replaced is placed in the vicinityof the center of a screen G1, and display regions 21 a to 21 h ofsimilar factors are placed on the periphery of the display region 22.Arrows connecting the display region 22 of the factor to be replaced tothe display regions 21 a to 21 h of the similar factors have lengths inaccordance with distances between factor values. Therefore, the user cangrasp the distances between a plurality of similar factors and thefactor to be replaced by seeing the screen shown in FIG. 8. Furthermore,the display regions 21 a to 21 h of the similar factors can be selectedby, for example, a cursor.

Furthermore, the similar factor space date may contain data indicatingthe distance (similarity) between the similar factors. In this case, themodel proposing part 11 can determine the positions of the displayregions 21 a to 21 h, also considering the distance between the similarfactors. Thus, the distance between the display regions can be set inaccordance with the distance between the similar factors. This enables asimilar factor with a small distance to be displayed closer to thefactor to be replaced, whereby the user can determine a replacementfactor easily.

In the case where there are a plurality of factors to be replaced, it ispreferred that similar factors are presented on a factor basis so as tobe selected. Alternatively, in the case where there are a plurality offactors to be replaced, the model proposing part 11 may repeat thefollowing two processings (1) and (2) with respect to a plurality offactors to be replaced. (1) The similar factor space creating part 91 iscaused to generate similar factor space data with one of a plurality offactors as a reference factor. (2) Similar factors are presented to theinformation processing apparatus 15 a so as to allow the user to selectthe similar factors.

The model proposing part 11 receives data indicating a similar factorselected by the user from the information processing apparatus 15 a.Then, a model obtained by replacing the factor to be replaced among thefactors included in the existing model by the similar factor selected bythe user is created as a replacement model. The replacement model iscomposed of, for example, data indicating factors after the replacementand data indicating a regression equation utilizing these factors. Themodel proposing part 11 sends the replacement model to the informationprocessing apparatus 15 a as support data (Op15).

Op13, Op14, and Op15 are processings in the case where the modelcondition data indicates that the replacement is performed on a factorbasis. On the other hand, in the case where the model condition dataindicates that the replacement is performed on a model basis (“on amodel basis” in Op12), the model proposing part 11 instructs the similarmodel space creating part 92 to create a similar model space with anexisting model as a reference model (Op16). Then, the model proposingpart 11 receives the similar model space data from the similar modelspace creating part 92. The similar model space data is, for example,data in which data specifying models similar to the reference model andthe distances of the similar models with respect to the reference modelare recorded so as to be associated with each other.

The processing of generating the similar model space data by the similarmodel space creating part 92 has various patterns. Herein, as anexample, the case where the creation of a model obtained by changing apart of factors of the reference model is requested will be describedbriefly. In this case, the similar model space creating part 92 requeststhe similar factor space creating part 91 to create a similar factorspace showing a group of factors similar to a part of the factors. Thesimilar model space creating part 92 creates a plurality of replacementmodel candidates obtained by replacing a part of the factors in thereference model by similar factors, based on the similar factor space.Then, the model proposing part 11 requests the distance calculating part8 to calculate the distances (similarities) between the reference modeland a plurality of replacement model candidates. Thus, the similar modelspace creating part 92 obtains information indicating a group ofreplacement models close to the reference model and the distancesthereof to generate similar model space data. The detail of theprocessing of generating the similar model space data will be describedlater.

The model proposing part 11 sends the similar model space dataindicating the similar models and the distances thereof to theinformation processing apparatus 15 a as support data, and causes theinformation processing apparatus 15 a to present the similar models tothe user as replacement models through a display apparatus (not shown)(Op17). At this time, it is preferred to present the similar models sothat the distances thereof to the reference model (=existing model) canbe grasped. Thus, the user can consider the similarity to the existingmodel for selecting a replacement model.

FIG. 9 is a diagram showing a screen display example of the similarmodel space data in the information processing apparatus 15 a. In theexample shown in FIG. 9, a display region 23 of an existing model isplaced in the vicinity of the center of a screen G2, and display regions24 a to 24 c of respective similar models (replacement models 1 to 3)are placed on the periphery of the display region 23. Arrows in solidlines connecting the display region 23 of the existing model to therespective display regions 24 a to 24 c of the similar models havelengths in accordance with distances between models. Furthermore, thedisplay region 23 of the existing model and the display regions 24 a to24 c of the replacement models 1 to 3 are connected to a region 33 andregions 34 a to 34 c showing the contents of the respective models bydotted lines. In the respective regions 33 and 34 a to 34 c, regressionequations of the models, the kinds of target phenomena, and factor namescorresponding to respective explanatory variables are displayed. Theuser seeing the screen shown in FIG. 9 can grasp the distances betweenthe respective replacement models 1 to 3 and the existing model, and thecontents of the models.

Due to the processing shown in FIG. 7, support data proposing anappropriate replacement factor or replacement model is output to theinformation processing apparatus 15 a that requests the creation of areplacement model of the existing model. Due to the same processing, areplacement model or a replacement factor with high reasonability isautomatically provided to the respective information processingapparatuses 15 a to 15 c.

The processing of the model proposing part 11 shown in FIG. 7 is anexample, and is not limited thereto. The data output to the informationprocessing apparatus 15 a as the support data may contain dataindicating factor values (a fitting degree enhancement property,long-term stability, commoness, or the like) of the replacement factorsin addition to the above.

Furthermore, in Op17, for example, only the similar model closest to theexisting model may be output, instead of outputting all the similarmodels indicated by the similar model space data. This makes theselection operation by the user unnecessary. Similarly, even in Op14 andOp15, only data indicating the similar factor closest to the factor tobe replaced may be output as data indicating a replacement factor,instead of outputting all the similar factors so as to allow the user toselect.

(Detail of Accumulation Processing: Extraction of an Inner-Model FactorValue)

Next, the detail of the accumulation processing of factor value data bythe inner-model factor value extracting part 71 will be described. FIG.10 is a flowchart illustrating an operation example of the inner-modelfactor value extracting part 71. The processing shown in FIG. 10 is theprocessing of the inner-model factor value extracting part 71 thatreceives an instruction from the model managing part 5 in Op4 shown inFIG. 6.

First, the inner-model factor value extracting part 71 receives aninstruction of extracting a factor value based on a change in a factorin a particular existing model from the model managing part 5 (Y inOp41). Then, the inner-model factor value extracting part 71 determineswhether or not there is a factor that has enhanced a fitting degree ofanalysis-estimation or a fitting degree of prediction result, comparedwith those before the change in the existing model (Op42). At this time,the inner-model factor value extracting part 71 acquires information onthe models before and after the change in the factor from the modelmanaging part 5. The information on the models contains, for example,the factors included in the models, target phenomena of the models,regression equations, fitting degrees of analysis-estimation or fittingdegrees of prediction result before and after the change, and thesignificance of each factor before and after the change. The detail ofthe determination processing in Op42 will be described later.

When Yes (Y) is determined in Op42, the inner-model factor valueextracting part 71 records the information on the factor that hasremarkably enhanced a fitting degree of prediction result or a fittingdegree of analysis-estimation in the factor value DB 65 as a factorhaving a “fitting degree enhancement property by a change in a factor”together with the peripheral information on the factor. As theperipheral information on the factor, for example, informationindicating target phenomena of the models before and after the change inthe factor, information indicating the models before and after thechange, a value showing the degree of a fitting degree enhancementproperty, and the like are recorded.

Furthermore, when the inner-model factor value extracting part 71receives an instruction of extracting a factor value based on a changein an application period in a particular existing model from the modelmanaging part 5 (Y in Op44), the inner-model factor value extractingpart 71 determines whether or not there is a factor that has enhanced afitting degree of analysis-estimation or a fitting degree of predictionresult, compared with those before the change in the existing model(Op45). At this time, the inner-model factor value extracting part 71acquires information on the models before and after the change in thefactor from the model managing part 5. The detail of the determinationprocessing in Op45 will be described later.

When Yes (Y) is determined in Op45, the inner-model factor valueextracting part 71 records information on the factor that has remarkablyenhanced a fitting degree of prediction result or a fitting degree ofanalysis-estimation in the factor value DB 65 as the factor having an“fitting degree enhancement property by a change in an applicationperiod” together with the peripheral information on the factor (Op46).As the peripheral information on the factor, for example, informationindicating target phenomena of the models before and after the change inthe factor, information indicating the model after the change, a valueshowing the degree of a fitting degree enhancement property, and thelike are recorded.

The proceedings in Op41 to Op44 are repeated until an instruction ofpassing through an event waiting loop is provided from the modelmanaging part 5 (until Y is determined in Op47). Thus, every time aninstruction of extracting a factor value is provided by the modelmanaging part 5, a factor having a fitting degree enhancement propertyby a change in a factor and a factor having a fitting degree enhancementproperty by a change in an application period are extracted and recordedin the factor value DB 65.

Herein, the detail of the processings in Op42 and Op45 will berespectively described successively. FIG. 11 is a flowchart illustratingthe detail of the processing in Op42. In the example shown in FIG. 11,first, the inner-model factor value extracting part 71 determineswhether a fitting degree used for evaluating a factor value is a fittingdegree of analysis-estimation or a fitting degree of prediction resultbased on the data acquired from the model managing part 5 (Op421).

In the case where the fitting degree is a fitting degree ofanalysis-estimation (“fitting degree of analysis-estimation” in Op421),the processings in Op422 to Op424 are performed. First, the inner-modelfactor value extracting part 71 determines whether or not a fittingdegree of analysis-estimation has changed largely before and after thechange in the factor of the model (Op422). The inner-model factor valueextracting part 71 may receive data indicating a change in a fittingdegree of analysis-estimation in the models before and after the changein the factor from the model managing part 5, or may refer to fittingdegrees of analysis-estimation corresponding to the respective instanceIDs of the models before and after the change, recorded in the modelinstance DB 63. Whether or not the value of a fitting degree ofanalysis-estimation has changed largely can be determined based on apredetermined threshold value, for example.

When it is determined that the fitting degree of analysis-estimation haschanged largely (Y in Op422), the inner-model factor value extractingpart 71 determines whether or not there is a large change in a t-testvalue of a weight (parameter) of the changed factor (hereinafter,referred to as the changed factor (Op424). The t-test value is anexample of data indicating the significance of a factor. The inner-modelfactor value extracting part 71 may receive a t-test value of eachweight from the model managing part 5 or from the model instance DB 63,in the same way as in the fitting degree of analysis-estimation.

When Yes (Y) is determined in Op423, the inner-model factor valueextracting part 71 determines whether or not the t-test values of theweights (parameters) of the factors that have not been changed increaseremarkably compared with those of the factors before the change (Op424).When No (N) is determined in Op424, the inner-model factor valueextracting part 71 determines that the changed factor has remarkablyincreased the fitting degree of analysis-estimation of the model afterthe change (Op428). Thus, the factor contributing to the enhancement ofthe fitting degree of analysis-estimation of the model after the changeis extracted.

Specific examples of the processings in Op422 to Op424 will bedescribed. FIG. 12 is a diagram conceptually showing a model before thechange in a factor and a model after the change in a factor. In FIG. 12,M1 represents a model before the change in the factor, and M2 representsa model after the change in the factor. In M1 and M2, Y in regressionequations is an objective variable, and a target phenomenon thereof isthe number of system troubles of a credit association. In the modelbefore the change represented by M1, β₀ is a weight (parameter) of aconstant term X₀, and β₁ to β₃ are respective weights of explanatoryvariables X₁ to X₃. The explanatory variables X₁, X₂, and X₃respectively represent day factors “beginning of next week”, “rainyseason”, and “Wednesday, Thursday, Friday in winter”. Numerical valuesin parentheses below the respective weights β₁ to β₃ show t-test valuesof the respective weights. The t-test value is an exemplary valueshowing the significance of each factor.

In the model after the change represented by M2, the factor “rainyseason” corresponding to the explanatory variable X₂ of the model beforethe change is changed to “late June to mid-July”. Due to the change inthe factor, the numerical values of the respective weights β₀ to β₃ inM2 also become different from those in M1. Then, the fitting degree ofanalysis-estimation is enhanced from “0.64” to “0.75”. Thus, it can bedetermined that the fitting degree of analysis-estimation has beenenhanced due to the change in the factor of the model. Furthermore, theweight of the factor “late June to mid-July” increases to “+13.2” withrespect to the t-test value “+1.8” of the weight of the factor “rainyseason”. Compared with this change amount “11.4”, the change amount ofthe t-test values of the weights of the other factors “beginning of nextweek” and “Wednesday, Thursday, Friday in winter” is 0.1 to 0.2, whichis ⅕ or less. In such a case, the inner-model factor value extractingpart 71 can determine that the enhancement of a fitting degree ofanalysis-estimation by a change in a factor of a model has occurred dueto the factor “late June to mid-July”.

In this case, in the factor value DB 65, for example, as in the recordD1 shown in FIG. 5, “late June to mid-July (s-012)” as a contributionfactor (explanatory variable ID), a “fitting degree enhancement (factorvalue change)” as an attribute, “m-002” (corresponding to the number ofsystem troubles in S branch office of A credit association: see A2 inFIG. 2) as an objective variable ID, “finance” as a category (field),respective model instance IDs of a model before the change and a modelafter the change, “2004” as an application period, and “0.11” asenhancement performance are recorded. Herein, the value “0.11” ofenhancement performance is a value obtained by calculating a changeamount (0.75-0.64) of a fitting degree of analysis-estimation, as anexample.

Next, proceedings in Op425 to Op437 in the case where a fitting degreeused for evaluating a factor value is determined to be a fitting degreeof prediction result in Op421 in FIG. 11 will be described. In thiscase, first, the inner-model factor value extracting part 71 determineswhether or not a fitting degree of prediction result has changed largelybefore and after the change in the factor of the model (Op425). Theinner-model factor value extracting part 71 may receive data indicatingfitting degrees of prediction result in the models before and after thechange in the factor from the model changing part 5, or may obtain thedata from the model instance DB 63.

When it is determined that the fitting degree of prediction result haschanged largely (Y in Op425), the inner-model factor value extractingpart 71 determines whether or not there is a large change in a fittingdegree by addition of the factor that has changed (hereinafter, referredto as a changed factor) (Op426). The fitting degree by addition is anexemplary value showing the significance of a factor. The fitting degreeby addition of one factor is obtained by calculating a fitting degree ofprediction result of a model with respect to each of the case where afactor is included in a model and the case where the factor is notincluded in the model, and obtaining a difference between the fittingdegrees of prediction result. The inner-model factor value extractingpart 71 may receive the fitting degree by addition of each factor beforeand after the change from the model managing part 5, or may acquire thefitting degree by addition from the model instance DB 63.

When Yes (Y) is determined in Op426, the inner-model factor valueextracting part 71 determines whether or not the fitting degrees byaddition of the factors that have not been changed increase remarkablycompared with those of the factors before the change (Op427). In thecase where Yes (Y) is determined in Op427, the inner-model factor valueextracting part 71 determines that the changed factor has remarkablyincreased the fitting degree of prediction result of the model after thechange (Op428). Thus, a factor contributing to the enhancement of thefitting degree of prediction result of the model after the change isextracted.

Specific examples of the processings in Op425 to Op427 will bedescribed. FIG. 13 is a diagram conceptually showing a model after thechange in a factor and a model after the change in the factor. In FIG.13, M3 represents a model before the change in the factor, and M4represents a model after the change in the factor. In M3 and M4,numerical values in parentheses below the respective weights β₁ to β₃show t-test values of the respective weights. In the model after thechange represented by M4, the factor “rainy season” corresponding to theexplanatory variable X₂ of the model before the change represented by M3is changed to “late June to mid-July”. Furthermore, a predictionapplication period 2005 of the model M3 before the change is 2006 in themodel M4 after the change. Then, the fitting degree of prediction resultis enhanced from “0.65” to “0.72”. The fitting degrees of predictionresult are actually measured values in 2005 and 2006 that are respectiveprediction application periods before and after the change.

The fitting degree by addition of the factor “late June to mid-July” isincreased to “0.41” with respect to the fitting degree by addition“0.24” of the factor “rainy season”. Compared with this change amount“0.17” of the fitting degree by addition, the change amounts of thefitting degrees by addition of the other factors “beginning of nextweek” and “Wednesday, Thursday, Friday in winter” are 0.02 to 0.04,which is ⅓ or less. In such a case, the inner-model factor valueextracting part 71 can determine that the enhancement of the fittingdegree of prediction result by the change in the factor of the model hasoccurred due to the factor “late June to mid-July”.

The detail of the processing (extraction processing of a factorcontributing to the enhancement of a fitting degree by the change in afactor) in Op42 shown in FIG. 10 is as described above. Due to the aboveprocessing shown in FIG. 11, a factor value can be extracted from bothpoints of view of a fitting degree of analysis-estimation and a fittingdegree of prediction result. Next, the processing in Op45, i.e., thedetail of the extraction processing a factor contributing to theenhancement of a fitting degree by the change in an application periodwill be described. FIG. 14 is a flowchart illustrating the detail of theprocessing in Op45.

In the example shown in FIG. 14, first, the inner-model factor valueextracting part 71 determines whether or not the changed applicationperiod is only a prediction application period based on the dataacquired from the model managing part 5 (Op451). In the case where onlythe prediction application period is changed, the processings in Op456to Op458 are performed.

On the other hand, in the case where the changed prediction applicationperiod is not only the prediction application period, the inner-modelfactor value extracting part 71 determines whether the fitting degreeused for evaluating a factor value is a fitting degree ofanalysis-estimation or a fitting degree of prediction result based onthe data acquired from the model managing part 5 (Op452). In the case ofthe fitting degree of prediction result, the processings in Op456 toOp458 are performed, and in the case of the fitting degree ofanalysis-estimation, Op453 to Op455 are performed. Specific exampleswill be illustrated for the respective cases.

FIG. 15 is a diagram conceptually showing models before and after achange in the case where the analysis application period is changed. InFIG. 15, M5 and M6 respectively show models before and after the changein the case where the analysis application period is changed from 2004to 2005. In this case, the analysis application periods are different,so that the weights β₀ to β₃ of M5 are different from the weights β₀ toβ₃ of M6, and fitting degrees of analysis-estimation also have differentvalues. The case where the fitting degrees of analysis-estimation areused as fitting degrees to be evaluated will be described below.

In this case, in Op452, it is determined that a fitting degree used forevaluation =an “fitting degree of analysis-estimation”. The inner-modelfactor value extracting part 71 compares the fitting degrees ofanalysis-estimation “0.64” and “0.72” before and after the change, anddetermines whether the fitting degree of analysis-estimation has changedby a predetermined value or more (Op453). When the fitting degree ofanalysis-estimation has changed by a predetermined value or more, afactor whose t-test value of a weight has remarkably increased beforeand after the change in the application period is extracted (Op454).Herein, for example, a factor “rainy season” of the weight β₂ whoset-test value has increased by 0.1 or more is extracted.

Furthermore, the inner-model factor value extracting part 71 determineswhether or not there is a large change in signs and t-test values of theweights β₁, β₃ of the factors “beginning of next week” and “Wednesday,Thursday, Friday in winter” other than the extracted factor (Op455). Forexample, it can be determined that there is a large change if the signof the weight has changed or the change amount of the t-test value is0.07 or more. In the case of No (N) in Op455, the inner-model factorvalue extracting part 71 determines that the extracted factor “rainyseason” is a factor enhancing the fitting degree of analysis-estimationof the model M6, more specifically, the factor having a fitting degreeenhancement property due to a change in an application period (Op459).

On the other hand, in FIG. 15, M7 and M8 respectively represent modelsbefore and after the change in the application period in the case wherethe analysis application period is changed from 2004 to 2005, andfurther, the prediction application period is changed from 2005 to 2006.More specifically, the respective weights β₀ to β₃ of the modelrepresented by M7 are calculated based on the actually measured valuesin 2004 (=an analysis application period, and a predicted value of “thenumber of system troubles of a credit association” in 2005 (=aprediction application period) of next year is calculated, using aregression equation of the weights β₀ to β₃. Similarly, the respectiveweights β₀ to β₃ of the model represented by M8 are calculated based onthe actually measured values in 2005 (=an analysis application period),and a predicted value in 2006 (=a prediction application period) of nextyear is calculated, using a regression equation of the weights β₀ to β₃.The fitting degrees of prediction result in M7 and M8 are respectivelyobtained based on the predicted value and the actually measured value in2005 and 2006. The case where the fitting degrees of prediction resultare used as fitting degrees to be evaluated will be described.

In this case, in Op452, it is determined that a fitting degree used forevaluation=“fitting degree of prediction result”. The inner-model factorvalue extracting part 71 compares fitting degrees of prediction result“0.65” and “0.72” before and after the change, and determines whether ornot the fitting degree of prediction result has changed by apredetermined value or more (Op456). If the fitting degree of predictionresult has changed by a predetermined value or more, a factor whosefitting degree by addition has increased remarkably before and after thechange in an application period is extracted (Op457). Herein, forexample, a factor “rainy season” whose fitting degree by addition hasincreased by 0.1 or more is extracted.

In the case of Yes (Y) in Op457, furthermore, the inner-model factorvalue extracting part 71 determines whether or not there is a largechange in fitting degrees by addition of the factors “beginning of nextweek” and “Wednesday, Thursday, Friday in winter” other than theextracted factor (Op458). For example, it can be determined that thereis a large change if the change amount of the fitting degree by additionis 0.05 or more. In the case of No (N) in Op458, the inner-model factorvalue extracting part 71 determines that the extracted factor “rainyseason” is a factor enhancing the fitting degree of prediction result ofthe model M8, i.e., a factor having a fitting degree enhancementproperty by a change in an application period (Op459).

FIG. 15 shows an example of the case where an analysis applicationperiod has been changed, and FIG. 16 is a diagram showing models beforeand after a change in the case where only the prediction applicationperiod is changed. In FIG. 16, the prediction application period of M9is 2005, and the prediction application period of M10 is 2006. Theanalysis application periods of M9 and M10 are both 2004. In this case,the analysis application periods are the same, so that the respectiveweights β₀ to β₃ of M9 have the same numerical values as those of theweights β₀ to β₃. In this case, an evaluation is made only with thefitting degree of prediction result, so that the processings (Op456 toOp457) in the case of evaluating the fitting degree of prediction resultare performed based on the determination in Op451 in FIG. 14.

The detail of the processing (extraction processing of a factorcontributing to the enhancement of a fitting degree by a change in anapplication period) in Op45 of FIG. 10 is as described above. Due to theabove processing shown in FIG. 14, a factor value can be extracted fromboth points of view: a fitting degree of analysis-estimation and afitting degree of prediction result.

(Detail of Accumulation Processing: Inter-Model Factor Value Extraction)

Next, the detail of the accumulation processing of factor value data bythe inter-model factor value extracting part 72 will be described. FIG.17 is a flowchart illustrating an operation example of the inner-modelfactor value extracting part 71. The processing shown in FIG. 17 isinter-model factor value extraction processing in the case where thereis an instruction from the model managing part 5 to the inter-modelfactor value extracting part 72 in Op6 shown in FIG. 6.

In the example shown in FIG. 17, when receiving an instruction from themodel managing part 5 (Y in Op61), the inter-model factor valueextracting part 72 determines whether or not there is a factor whosecontribution ratio with respect to the fitting degree information of aplurality of models having different application periods with onephenomenon as a target is a predetermined value or more over a pluralityof application periods (over a long period of time) (Op62).

The inter-model factor value extracting part 72 can perform thedetermination based on the data in the model instance DB 63. Forexample, the inter-model factor value extracting part 72 refers to allthe records having the same objective variable ID from the modelinstance DB 63, and can use a group of factors included in modelsindicated by the records and the significance of each factor for theabove determination. Thus, a factor having high commoness andcontributing to the enhancement of a fitting degree in a plurality ofmodels having the same objective variable can be extracted.

When Yes (Y) is determined in Op62, the inter-model factor valueextracting part 72 records the factor extracted in Op62 in the factorvalue DB 65 as the factor having long-term stability together with theperipheral information on the factor (Op63). As the peripheralinformation on the factor, for example, the information indicating atarget phenomenon of models in which the factor contributes to theenhancement of a fitting degree over a long period of time, and anumerical value showing the degree of a fitting degree enhancementproperty are recorded.

The inter-model factor value extracting part 72 determines whether ornot there is a factor contributing to the enhancement of a fittingdegree commonly in a plurality of models having the same field, region,and application period (Op64). The inter-model factor value extractingpart 72 can perform the determination based on the data in the modelinstance DB 63 and the objective variable DB 61. For example, theinter-model factor value extracting part 72 acquires an objectivevariable ID having the same category (field) and category (region) fromthe objective variable DB 61, and refers to records having the objectivevariable ID and the same application period from the model instance DB63. The inter-model factor value extracting part 72 acquires a group offactors included in the models indicated by the records, and thesignificance of each factor, thereby extracting a factor having highcommoness in the models, and contributing to the enhancement of afitting degree.

When Yes (Y) is determined in Op64, the inter-model factor valueextracting part 72 records the factor extracted in Op64 in the factorvalue DB 65 as the factor having commoness together with the peripheralinformation on the factor (Op65).

The processings in Op62 to Op65 are repeated until an instruction ofpassing through an event waiting loop is provided from the modelmanaging part 5 (until Y is determined in Op66). Thus, every time aninstruction of extracting an inter-model factor value is provided by themodel managing part 5, a factor having long-term stability and a factorhaving commoness are extracted and recorded in the factor value DB 65.

Next, specific examples of the processing of extracting a factor havinglong-term stability (Op62) and the processing of extracting a factorhaving commoness (Op64) will be described successively. FIG. 18 is aflowchart illustrating a specific example of the processing in Op62. Inthe example shown in FIG. 18, the inter-model factor value extractingpart 62 collects a group of models that have been applied for a longperiod of time with one phenomenon as a target, with reference to themodel instance DB 63 (Op621). For example, the inter-model factor valueextracting part 72 extracts a plurality of model groups having the sameobjective variable ID and having different analysis application periods.Then, among the extracted model groups, a model group, in which a totalof analysis application periods of the respective models included in themodel group is longer than a predetermined value, is determined as thegroup of models that have been applied for a long period of time.

FIG. 19 is a conceptual diagram showing an example of such a group ofmodels. In a group of models M11 to M15 shown in FIG. 19, each left sideshows a target phenomenon of a model corresponding to an objectivevariable of a regression equation, and each right side shows factorsrespectively corresponding to explanatory variables and a constant termof the regression equation. β₀ to β₃ represent the respective weights ofthe constant terms and the explanatory variables. Furthermore, undereach objective variable, an analysis application period and a fittingdegree of analysis-estimation are described, and under each term on theright side, a t-test value of each the weights β₀ to β₃ is described.

In the group of models M11 to M15, “sales of I town S toy store” is setto be a target phenomenon (objective variable), and analysis applicationperiods are different from each other: 2000, 2001, 2002, 2003, and 2004.

The inter-model factor value extracting part 72 extracts a factor whichhas commoness and whose t-test value is stable at a predetermined levelor more among the group of models M11 to M15 (Op622 in FIG. 18). Herein,whether or not the commoness of a factor is high is determined, forexample, based on a ratio at which models including the factor occupy inthe extracted group of models M11 to M15. Whether or not the t-testvalue of the factor is stable at a predetermined level or more isdetermined, for example, based on whether or not the t-test value of thefactor is always equal to or more than a predetermined threshold value.Furthermore, it may be determined that the t-test value of the factor isstable at a predetermined level or more in the case where the sign(positive/negative) of a coefficient of the factor is constant, and theabsolute value of a t-value is not below a predetermined high value. Theinter-model factor value extracting part 72 records the factor extractedin Op623 in the factor value DB 65 as the factor having long-termstability (Op623).

Herein, in the example shown in FIG. 19, a group of factors having ahigh inter-factor similarity among factors included in the group ofmodels M11 to M15 can also be dealt with as the same factor. In theexample shown in FIG. 19, factors “autumnal equinox weekend factor”,“autumnal equinox Saturday factor”, and “autumnal equinox Sunday factor”have a high similarity, so that they may be used as the same factor. Thecalculation of a similarity between factors will be described later.

In this case, for example, assuming that the threshold value of a t-testvalue is 5, the group of the factor “autumnal equinox weekend factor”,and the factors “autumnal equinox Saturday factor” and “autumnal equinoxSunday factor” which are dealt with as the same factors as the “autumnalequinox weekend factor” has t-test values that always exceed thethreshold value. Therefore, these factors are extracted in Op 622.Furthermore, the other factors “sales of goods of last week” and“holiday factor” are not extracted since the t-test values do not exceed5 although they have commoness.

Therefore, the inter-model factor value extracting part 72 records theextracted factors “autumnal equinox weekend factor”, “autumnal equinoxSaturday factor”, and “autumnal equinox Sunday factor” in the factorvalue DB 65 as the factor having long-term stability.

In the factor value DB 65, for example, as in the record D3 shown inFIG. 5, “autumnal equinox weekend factor, autumnal equinox Saturdayfactor, autumnal equinox Sunday factor (s-031, s-101, s-017)” ascontribution factors (explanatory variable IDs), “long-term stability”as an attribute, “m-006” as an objective variable ID (objective variableID of sales of I town S toy store”, “retail” as a category (field), aninstance ID of a typical model, “2000 to 2004” as an applicable period,and “0.78” as an average fitting degree are recorded. The averagefitting degree “0.78” can be set to be, for example, an average value offitting degrees of analysis-estimation of models in the applicableperiod of 2000 to 2007. Furthermore, the numerical value indicating theperformance degree of long-term stability also includes, for example, anaverage t-value of the respective contribution factors without beinglimited to an average fitting degree.

Furthermore, the typical model can be set to be the model M11 having ahighest t-test value of the factors “autumnal equinox weekend factor,autumnal equinox Saturday factor, autumnal equinox Sunday factor” amonga plurality of models M11 to M15 in which the factors contribute to theenhancement of a fitting degree commonly.

Next, a specific example of the processing (Op64 in FIG. 17) ofextracting a factor having commoness will be described. FIG. 20 is aflowchart illustrating a specific example of the processing in Op64. Inthe example shown in FIG. 20, the inter-model factor value extractingpart 72 first acquires a group of objective variable IDs having the samecategory (field) and category (region), with reference to the objectivevariable DB 61. The inter-model factor value extracting part 72 extractsrecords of a group of models having objective variables represented bythe group of objective variable IDs and having the same applicationperiod from the model instance DB 63. Thus, the records of a group ofmodels belonging to the same field and region and having the sameapplication period are collected (Op641).

FIG. 21 is a conceptual diagram showing an example of such a group ofmodels. In the group of models M16 to M18 shown in FIG. 21, each leftside shows a target phenomenon of a model corresponding to an objectivevariable of a regression equation, and each right side shows factorsrespectively corresponding to explanatory variables and a constant termof the regression equation. β₀ to β₃ represent respective weights of theconstant term and the explanatory variables. Furthermore, under eachobjective variable, an analysis application period and a fitting degreeof analysis-estimation are described, and under each term on the rightside, a t-test value of each of the weights β₀ to β₃ is described.

In the models M16, M17, and M18, “sales of I town S toy store”, “salesof I town Y Buddhist altar fittings store”, and sales of I town HJapanese clothes store” are respectively set to be target phenomena. Thephenomena belong to the same category (region) “I town” and the samecategory (field) (private management). Furthermore, each analysisapplication period of the group of models M16 to M18 is 2004.

Herein, although a group of models having the same field, region, andapplication period are collected, a group of models to be collected arenot limited thereto. For example, in the case where the inter-modelfactor value extracting part 72 desires to extract a group of modelshaving commoness over a certain field, the inter-model factor valueextracting part 72 can collect a group of models having an objectivevariable of a category (field) without limiting a region and anapplication period.

The inter-model factor value extracting part 72 extracts a factor whosecommoness is high and whose t-test value is stable at a predeterminedlevel or more (for example, a predetermined threshold value or more),among such a group of models M16 to M18 (Op642 in FIG. 20). Theinter-model factor value extracting part 72 records the extracted factorin the factor value DB 65 as a factor having a “common value”.

Thus, a factor having high commoness can be found in a group of modelshaving the same field, region, and application period, for example.

Herein, in the example shown in FIG. 21, a group of factors having ahigh inter-factor similarity among the factors included in a group ofmodels M16 to M18 can also be dealt with as the same factor. In theexample shown in FIG. 21, since the factors “autumn equinox weekendfactor” and “autumn equinox Saturday factor” have a high similarity, sothat they may be used as the same factor.

In this case, for example, assuming that the threshold value of a t-testvalue is 5, a group of the factor “autumn equinox weekend factor” andthe factor “autumn equinox Saturday factor” which is dealt with as thesame factor as the “autumn equinox weekend factor” has t-test valuesthat always exceed the threshold value. Therefore, these factors areextracted in Op642. Furthermore, although the factor “sales of goods oflast week” has commoness, the t-test value thereof does not exceed 5, sothat this factor is not extracted. “Holiday factor” and “luckiest dayfactor” have no commoness.

The inter-model factor value extracting part 72 records the extractedfactors “autumn equinox factor” and “autumn equinox Saturday factor” inthe factor value DB 62 as the factors having commoness.

In the factor value DB 65, for example, as in the record D4 shown inFIG. 5, “autumn equinox weekend factor, autumn equinox Saturday factor(s-031, s-101)” as contribution factors (explanatory variables IDs),“commonness” as an attribute, “m-006, m-043, m-0462” (respectiveobjective variable IDs of sales of I town S toy store, Y Buddhist altarfittings store, and H Japanese clothes store) as objective variable IDs,“private management” as a category (field), an instance ID of a typicalmodel, the number of adopted spots “351”, a field adoption ranking “1stplace” and an average fitting degree “0.78” are recorded. As the numberof adopted spots, the number of models including the factors, forexample, among a group of models extracted in Op641 can be used. Inorder to calculate a field adoption ranking, the inter-model factorvalue extracting part 72 acquires, for example, an objective variable IDthat is a category (field)=“private management” in the objectivevariable DB 61, and matches the record including the objective variableID with the model instance DB 63, whereby, the number of adoptions ofthe factors “autumn equinox weekend factor” and “autumn equinox Saturdayfactor” in the field can be obtained.

(Detail of Model Creation Processing: Similar Factor Space CreationProcessing)

Next, a specific example of the processing will be described in whichthe similar factor space creating part 91 receives an instruction fromthe model proposing part 11, and creates similar factor space data. FIG.22 is a flowchart illustrating an example of the processing in which thesimilar factor space creating part 91 creates similar factor space data.The processing shown in FIG. 22 is the processing of the similar factorspace creating part 91 that has received an instruction from the modelproposing part 11 in Op13 in FIG. 7.

First, the similar factor space creating part 91 receives a referencefactor and search conditions together with an instruction of creatingsimilar factor space data from the model proposing part 11 (Op1401). Thesearch conditions include, for example, data indicating a range in whichsimilar factors are searched for. The instruction of creating similarfactor space data may be provided by, for example, the similar modelspace creating part 92, instead of the model proposing part 11.

The similar factor space creating part 91 inquires about the informationindicating a factor value of the reference factor with respect to thefactor value DB 65. Herein, as an example, the case where the referencefactor is a day factor indicating “beginning of next week” will bedescribed. For example, in the case where the factor “beginning of nextweek” is recorded as the factor having long-term stability in the factorvalue DB 65, the similar factor space creating part 92 acquires dataindicating an attribute “long-term stability” of the factor value anddata indicating the enhancement of performance thereof as factor valueinformation.

The similar factor space creating part 91 acquires informationindicating a group of factors having the same attribute as that of theacquired factor value of the reference factor from the factor value DB65 (Op1403). Herein, a group of factors having the attribute “long-termstability” of the factor value is acquired. Then, the similar factorspace creating part 91 narrows down the factors to be replacementcandidates based on the search conditions received in Op1401 from thegroup of factors (Op1404). Hereinafter, the factors that have beennarrowed down will be referred to as similar factors.

After that, the distance calculating part 8 is requested to calculate adistance in factor values between the reference factor and each similarfactor (Op1405). The distance between the factor values is, for example,a numerical value showing the similarity in a degree of contribution tothe enhancement of a fitting degree between two factors. An example of acalculation of the distance between the factor values by the distancecalculating part 8 will be described later. The calculation of adistance in factor values between the factor and the reference factor bythe distance calculating part 8 is repeated over the whole similarfactors narrowed down in Op1404 (Op1406).

The similar factor space creating part 91 maps the similar factors in avirtual space based on the distance between the factor values calculatedby the distance calculating part 8 (Op1407). For example, the similarfactor space creating part 91 can place the respective similar factorsin a two-dimensional virtual space, with the position of the referencefactor as an origin. At this time, the similar factor space creatingpart 91 causes the distance calculating part 8 to calculate a distancebetween similar factors, regarding those which have a distance from thereference factor in a predetermined range (Op1408). The distancecalculated herein may be a distance between factor values or a distanced_(f) between the factors.

Table 2 shows an example of a distance between similar factorscalculated by the similar factor space creating part 91. In Table 2,“rainy season” is a reference factor, and the other factors are a groupof similar factors. The group of similar factors correspond to the groupof similar factors shown in Table 1.

TABLE 2 Summer Summer solstice Early solstice Late Early Rainy or earlysummer or Early Summer or early June to July to Factor name seasonsummer midsummer July solstice summer mid-July July mid-July ID 22 21a21b 21c 21d 21e 21f 21g 21h Rainy 22 0 24 43 43 43 46 25 43 37 seasonSummer 21a 24 0 41 53 65 49 60 47 40 solstice or early summer Early 21b43 41 0 25 46 55 86 80 79 summer or midsummer Early July 21c 43 53 25 022 41 80 78 84 Summer 21d 43 65 46 22 0 33 71 77 87 solstice Summer 21e46 49 55 41 33 0 38 42 56 solstice or early summer Late June 21f 25 6086 80 71 38 0 22 46 to mid-July July 21g 43 47 80 78 77 42 22 0 41 EarlyJuly 21h 37 40 79 84 87 56 46 41 0 to mid-July

The similar factor space creating part 91 can place the similar factorsin a virtual space in accordance with the distance between each similarfactor and the reference factor calculated in Op1405 and the distancebetween the similar factors as shown in Table 2. For example, a distanceof each similar factor from the origin in the virtual space isdetermined, for example, in accordance with the distance from thereference factor calculated in Op1405. Then, the positional relationshipon the relative virtual space between the similar factors is determinedso as to keep the distance between the similar factors shown in Table 2.Thus, the arrangement of the similar factors in the two-dimensionalvirtual space is determined. When the distance between the respectivesimilar factors in Table 2 is reflected to the virtual space, forexample, the respective similar factors are arranged as shown in FIG. 8.Similar factor space data indicating the arrangement of similar factorson the virtual space thus determined is generated (Op1409). As shown inTable 1, the data on a group of similar factors contains information ona target phenomenon of a model including each similar factor, a commonfactor value, the names of the similar factors, the distance from thereference factor (distance between the factor values), and the like.Furthermore, the data on the group of similar factors may contain dataindicating the distance between similar factors as shown in Table 2. Thesimilar factor space creating part 91 returns the data to the modelproposing part 11 as similar factor space data.

Thus, due to the processing shown in FIG. 22, information on a group offactors that have the same factor value as that of the reference factorand that are close to the reference factor is obtained. Consequently,information indicating similar factors that can contribute to theenhancement of a fitting degree of a model of the reference factor isobtained. The similar factor space data is not limited to the aboveexample, and the similar factor space data may contain any of the otherinformation, as long as the data contains information indicating similarfactors and information indicating the distance in factor values betweeneach similar factor and the reference factor. Furthermore, theprocessing of creating a similar factor space is not limited to theexample shown in FIG. 22, either. For example, in the example shown inFIG. 22, the processing of narrowing down similar factor based on thedistance in factor values between each similar factor and the referencefactor may be further performed.

(Distance Calculation: Example of Distance Calculation Between FactorValues)

Herein, an example of calculation of a distance between two factorvalues by the distance calculating part will be described. FIG. 23 is aflowchart illustrating an operation example of calculating a distanced_(v) in factor values between a factor “a” and a factor “b”. Thedistance calculating part 8 first calculates the distance d_(f) betweenthe factor “a” and the factor “b” (Op81).

The distance d_(f) shows, for example, to which degree the numericalvalues of explanatory variables corresponding to the respective factorsare matched. Specifically, the distance d_(f) can be expressed by acorrelation of numerical values of explanatory variables, as in anexample described later.

Furthermore, the distance calculating part 8 calculates a distance d_(t)in performance between the factor “a” and the factor “b” (Op82). Thedistance d_(t) shows, for example, to which degree the numerical valuesshowing the significance of the respective factors are matched. Thespecific example thereof will be described later.

Furthermore, the distance calculating part 8 calculates a distance d_(M)in model attributes between the model to which the factor “a” belongsand the model to which the factor “b” belongs (Op83). The distance d_(M)between model attributes is a numerical value showing the similaritybetween the attribute of the model of the factor “a” and the attributeof the model of the factor “b”. The attribute of the model includes, forexample, an objective variable of the model, explanatory variablesthereof, an application period thereof, and a fitting degree thereof.The distance d_(M) between model attributes is calculated respectivelyfor these attributes. The information indicating the attributes of themodels which the factors “a” and “b” belong to can be acquired from themodel instance DB 63 based on the respective instance IDs of the factors“a” and “b” recorded in the factor value DB 65.

The distance calculating part 8 obtains the distance d_(v) in factorvalues between the factor “a” and the factor “b” by substituting theabove distances d_(f), d_(t), and d_(M) into a previously incorporatedfunction f (d_(f), d_(t), d_(M)) (Op84). The function f(d_(f), d_(t),d_(M)) calculates a distance incorporating the distances d_(f), d_(t),and d_(M) totally, and is not particularly limited. As an example, thefunction f can be represented by the following Expression (4) or (5). Inthe following Expressions (4) and (5), K_(f), K_(t), and K_(M) arecoefficients showing the respective weights of the distances d_(f),d_(t), and d_(M).K _(f) ·d _(f) +K _(t) ·d _(t) +K _(M) ·d _(M)  (4){(K _(f) ·d _(f))²+(K _(t) ·d _(t))²+(K _(M) ·d _(M))²}^(1/2)  (5)

Hereinafter, a specific example of the calculation of the abovedistances d_(f), d_(t), and d_(M) will be described. Herein, an examplewill be described in the case where factors “rainy season” and “lateJune to mid-July” in the models represented by M1 and M2 in FIG. 12 arerespectively the factors “a” and “b”.

The distance calculating part 8 can calculate the distance d_(f) betweenthe factors, for example, by the following Expression (6).d _(f)=1−(square of correlation coefficient of explanatory variable offactor “a” and explanatory variable of factor “b”)  (6)

Expression (6) corresponds to the following Expression (7) in thepresent example.d _(f)=1−(square of correlation coefficient of vector of “rainy season”and vector of “late June to mid-July”)  (7)

The following Table 3 shows examples of element values in the case wherethe explanatory variables of these factors “rainy season” and “late Juneto mid-July” are expressed by vectors. The values of the explanatoryvariables of the factors “a” and “b” are, for example, recorded in arecord C100 of an element value table of the explanatory variable DB 62shown in FIG. 4.

TABLE 3 Rainy season Mid-June to Mid-July January 1 0 0 • 0 0 • 0 0 June1 0 0 • 0 0 June 8 1 0 • 1 0 June 10 1 1 • 1 1 July 20 1 1 • 0 0 July 310 0 • 0 0 • 0 0 December 31 0 0 Total applicable days 43 36

The distance calculating part 8 calculates a correlation of respectivevectors of “rainy season” and “mid-June to mid-July” having the elementvalues shown in the above Table 3, thereby calculating the aboveExpression (7). As a result, Distance d_(f)=1−0.9216=0.09784 isobtained. The distance d_(f) thus calculated has a value from 0 to 1,and as the value is smaller, the distance becomes smaller.

Furthermore, the distance calculating part 8 calculates the distanced_(t) by the following Expression (8).

$\begin{matrix}{d_{t} = {1 - {\frac{\begin{matrix}{\min\left( {{t - {{value}\mspace{14mu}{of}\mspace{14mu}{``{{rainy}\mspace{14mu}{season}}"}}},} \right.} \\\left. {t - {{value}\mspace{14mu}{of}\mspace{14mu}{``{{mid}\text{-}{June}\mspace{14mu}{to}\mspace{14mu}{mid}\text{-}{July}}"}}} \right)\end{matrix}}{\begin{matrix}{\max\left( {{t - {{value}\mspace{14mu}{of}\mspace{14mu}{``{{rainy}\mspace{14mu}{season}}"}}},} \right.} \\\left. {t - {{value}\mspace{14mu}{of}\mspace{14mu}{``{{mid}\text{-}{June}\mspace{14mu}{to}\mspace{14mu}{mid}\text{-}{July}}"}}} \right)\end{matrix}}}}} & (8)\end{matrix}$

Since a t-test value of “rainy season” is 1.8, and a t-test value of“mid June to mid-July” is 13.2, the distance d_(t) is calculated asrepresented by the following Expression (9). The distance d_(t) thuscalculated also has a value from 0 to 1, and as the value is smaller,the distance becomes smaller.

$\begin{matrix}{d_{t} = {{1 - \frac{{\min\left( {1.8,13.2} \right)}}{{\max\left( {1.8,13.2} \right)}}} = {{1 - \frac{1.8}{13.2}} = 0.864}}} & (9)\end{matrix}$

Furthermore, the distance calculating part 8 calculates the distancebetween the attribute of the model represented by M1 in FIG. 12, and theattribute of the model represented by M2 in FIG. 12, as the distanced_(MZ) between model attributes. Herein, as an example of the distanced_(MZ), the case of calculating a distance d_(MZ-y) between objectivevariables, a distance d_(MZ)-_(x) between explanatory variables, adistance d_(MZ)-_(B) between weights (β), a distance d_(MZ)-_(at)between analysis application periods, and a distance between fittingdegrees of analysis-estimation d_(MZ)-_(Aq) will be described.

Assuming that the vectors of the respective objective variables of themodels M1 and M2 are Y₁ and Y₂, the distance d_(MZ)-_(y) between theobjective variables can be calculated, for example, by the followingExpression (10).

$\begin{matrix}{d_{{MZ} - Y} = {{1 - {{{inner}\mspace{14mu}{product}}}} = {1 - {\frac{\left( {Y_{1} \cdot Y_{2}} \right)}{{Y_{1}} \cdot {Y_{2}}}}}}} & (10)\end{matrix}$

Furthermore, the distance calculating part 8 may acquire, for example,the objective variable IDs of the respective models M1 and M2, comparedata associated with the respective objective variable IDs of the modelsM1 and M2 in the objective variable DB 61, and may reflect a comparisonresult to the above distance d_(MZ)-_(y). For example, the distancecalculating part 8 may decrease the distance d_(MZ-y) if the phenomenonrepresented by the objective variable of M1 is the same as thephenomenon represented by the objective variable of M2.

The distance d_(MZ)-_(x) between the explanatory variables iscalculated, for example, by calculating the distance d_(f) between thefactors with respect to all the explanatory variables X₁ to X₃ includedin the model and adding up them. The distance d_(MZ)-_(B) between theweights (β) is calculated, for example, by the following Expression(11).

$\begin{matrix}{d_{{MZ} - B} = {1 - {\frac{\min\left( {\beta_{A},\beta_{B}} \right)}{\max\left( {\beta_{A},\beta_{B}} \right)}}}} & (11)\end{matrix}$

The distance d_(MZ)-_(at) between analysis application periods can becalculated using, for example, the following Expression (12), assumingthat the analysis application period of the model M1 is A and theanalysis application period of the model M2 is B. “Maximum time distanceof AB” in Expression (12) is a period between an earlier time of an Astart time or a B start time, and a later time of an A end time and a Bend time. Furthermore, the distance of a prediction application periodcan also be calculated similarly using Expression (12).

$\begin{matrix}{d_{{MZ} - {at}} = {2*\frac{{{Period}\mspace{14mu}{of}\mspace{14mu} A{ + }{Period}\mspace{14mu}{of}\mspace{14mu} B}}{{{Maximum}\mspace{14mu}{time}\mspace{14mu}{distance}\mspace{14mu}{of}{\mspace{11mu}\;}{AB}}}}} & (12)\end{matrix}$

The distance d_(MZ)-_(aq) between fitting degrees of analysis-estimationcan be calculated, for example, using the following Expression (13),assuming that the fitting degree of analysis-estimation of the model M1is a fitting degree A, and the fitting degree of analysis-estimation ofthe model M2 is a fitting degree B. The fitting degree can be calculatedusing the following Expression (13).

$\begin{matrix}{d_{{MZ} - {aq}} = {1 - \frac{\min\left( {{{fitting}{\mspace{11mu}\;}{degree}\mspace{14mu} A},{{fitting}\mspace{14mu}{degree}\mspace{14mu} B}} \right)}{\max\left( {{{fitting}{\mspace{11mu}\;}{degree}\mspace{14mu} A},{{fitting}\mspace{14mu}{degree}\mspace{14mu} B}} \right)}}} & (13)\end{matrix}$

The distance d_(f) between factors thus calculated, the distance d_(t)in performance between the factors, and the distance d_(MZ)(d_(MZ)-_(y), d_(MZ)-_(x), d_(MZ)-_(B), d_(MZ)-_(at)) between therespective attributes of the models to which the factors belong arecalculated. The distance calculating part 8 substitutes these distancesd_(t), d_(t), and d_(MZ) into a predetermined function f (d_(f), d_(t),d_(MZ)), thereby calculating the distance d_(v) between factor values in“rainy season” and “mid-June to mid-July”.

As described above, by calculating the distance d_(v) between the factorvalues, using the distance d_(t) in performance between the factors andthe distance d_(MZ) between the respective attributes of the models, inaddition to the distance d_(f) between the factors, the distance d_(v)between the factor values incorporating the contribution degree of thefactors with respect to the models to which the factors belong can becalculated. A method for calculating the distance between factor valuesis an example, and is not limited thereto. The distance calculating part8 may calculate the distance d_(v) between the factor values, furtherusing the other factor value information and model information.

(Detail of Model Creation Processing: Similar Model Space CreationProcessing)

Hereinafter, a specific example of the processing will be described, inwhich the similar model space creating part 92 receives an instructionfrom the model proposing part 11 and creates similar model space data.FIG. 24 is a flowchart illustrating an example of the processing inwhich the similar model space creating part 92 creates similar modelspace data. The processing shown in FIG. 24 is the processing of thesimilar model space creating part 92 that receives an instruction fromthe model proposing part 11 in Op16 shown in FIG. 7.

The similar model space creating part 92 first receives a referencemodel and search conditions together with an instruction of creatingsimilar model space data from the model proposing part 11 (Op1601). Thesearch conditions include, for example, data indicating a factorrequested to be replaced among the factors of the reference model, dataindicating the range of models to be searched for, and the like.

The similar model space creating part 92 first determines whether or notthere is a factor requested to be replaced in the reference model basedon the search conditions (Op1602). More specifically, the similar modelspace creating part 92 determines whether or not it is necessary to takeover at least a part of the factors in the reference model.

In the case where there is no factor requested to be replaced (N inOp1602), the similar model space creating part 92 extracts data onmodels similar to the reference model among those represented by therespective records in the model instance DB 63, and creates similarmodel space data.

In the case where there is a factor requested to be replaced (Y inOp1602), the similar model space creating part 92 determines whether ornot the range of models to be searched for, designated by the searchconditions, is limited to actually existing models (Op1604). In the casewhere the range is limited to only the actually existing models (Y inOp1604), the similar model space creating part 92 extracts data on aplurality of different groups of replacement models configured byreplacing factors of the reference model, which are similar to thereference model, from the models represented by the respective recordsin the model instance DB 63 (Op1605). At this time, the similarity(distance) between each replacement model and the reference model isalso calculated. Similar model space data is created based on thedistance and the data on the groups of replacement models.

Furthermore, in the case where the range of the models to be searchedfor, designated by the search conditions, is limited to only virtualmodels (N in Op1604 and Y in Op1606), the similar model space creatingpart 92 creates data indicating a plurality of different groups ofvirtual models configured by replacing a factor of the reference model,and extracts the data on the group of models similar to the referencemodel from the created data as data on a group of replacement models(Op1607). At this time, the similarity (distance) between eachreplacement model and the reference model is also calculated. Similarmodel space data is created based on the distance and the data on thegroup of replacement models.

Furthermore, in the case where the range of the models to be searchedfor, designated by the search conditions, is not limited to only theactually existing models or only the virtual models (N in Op1606), thesimilar model space creating part 92 executes the processings in Op1605and Op1607, and re-arranges the respectively obtained data on the groupof replacement models in the mass, thereby generating similar modelspace data (Op1608).

Thus, due to the processing shown in FIG. 24, similar model space datacan be generated in accordance with the replacement factor and the rangeof the models to be searched for, designated by the search conditions.Furthermore, information on a replacement model that is similar to theproperty of the reference model and is highly expected to enhance theperformance is obtained. Next, the detail of the processings in Op1603,Op1605, and Op1607 will be described successively.

(Detail of Similar Model Space Creation Processing [Op1603])

FIG. 25 is a flowchart illustrating the detail of the processing inOp1603 shown in FIG. 24. In the example shown in FIG. 25, the similarmodel space creating part 92 calculates the distance (i.e., distance dybetween objective variables) between the objective variable of a modelindicated by each record in the model instance DB 63 and the objectivevariable of the reference model (Op31). The calculation of the distancedy can be performed in the same way as in the above calculation of thedistance d_(MZ)-_(y). A model having an objective variable whosedistance d_(y) to the objective variable of the reference model is athreshold value or less is considered to be similar to the referencemodel and is extracted as a replacement model.

The similar model space creating part 92 causes the distance calculatingpart 8 to calculate an inter-model distance d_(m) between eachreplacement model and the reference model (Op32). A method forcalculating the inter-model distance d_(m) will be described later.Furthermore, the similar model space creating part 92 also causes thedistance calculating part 8 to calculate an inter-model distance d_(m)between replacement models (Op33). Then, similar model space data isgenerated based on the inter-model distance d_(m) (Op34). The similarmodel space data contains, for example, data indicating each replacementmodel, data indicating the distance between each replacement model andthe reference model, and data indicating the distance betweenreplacement models.

Table 4 shows an example of an inter-model distance between replacementmodels.

TABLE 4 Reference model Replacement (S branch office model 1 (N branchReplacement Replacement of E credit office of M credit model 2 (M branchmodel 3 (T branch Model association) association) office of S bank)office of C bank) ID 23 24a 24b 24c Reference model 23 0 33 29 32 (Sbranch office of E credit association) Replacement 24a 33 0 57 66 model1 (N branch office of M credit association) Replacement 24b 29 57 0 67model 2 (M branch office of S bank) Replacement 24c 32 66 67 0 model 3(T branch office of C bank)

Furthermore, the similar model space creating part 92 may arrangereplacement models on a two-dimensional virtual space with the referencemodel as an origin based on the distances calculated in Op32 and Op33,and include a coordinate of each replacement model in similar data spacedata. For example, the distance from the origin on the virtual space ofeach replacement model is determined in accordance with the inter-modeldistance between the reference model and each replacement modelcalculated in Op32. Then, the coordinate of each replacement model inthe virtual space is determined in accordance with the inter-modeldistance between the respective replacement models as shown in Table 4.The screen display example shown in FIG. 9 shows the arrangement on thevirtual space determined from the inter-model distance shown in Table 4.

As described above, as a result of the processing shown in FIG. 25, areplaceable model close to the reference model is extracted from theactually existing models represented by the respective records recordedin the model instance DB 63, and similar data space data indicatingthese models is generated.

(Calculation Example of Inter-Model Distance)

Next, a calculation example of an inter-model distance performed even inOp32 shown in FIG. 25 will be described. Herein, a calculation exampleof a inter-model distance d_(m) between a model Ma and a model Mb by thedistance calculating part 8 will be described. FIG. 26 is a flowchartillustrating an operation example in which the distance calculating part8 calculates the distance d_(m) between the models Ma and Mb. Thedistance calculating part 8 first acquires each attribute of the modelsMa and Mb from the model instance DB 63 (Op801). The distancecalculating part 8, for example, receives instance IDs of the models Maand Mb and refers to the data corresponding to the respective instanceIDs from the model instance DB 63, thereby acquiring informationindicating attributes of the models. As the attributes, for example, anobjective variable Y of a model, explanatory variables X₁ to X_(n),weights β₁ to β_(n), an application period, and a fitting degree areacquired.

The distance calculating part 8 calculates the distances between therespective obtained attributes of the model Ma and the respectivecorresponding attributes of the model Mb (Op802). Herein, an example ofthe distance between the respective attributes will be described withrespective to FIG. 27. FIG. 27 is a diagram conceptually showing thedistance between the respective attributes of the models Ma and Mb. Thedistance d_(y) between the objective variables shown in FIG. 27 is anumerical value indicating the similarity between the objective variableof the model Ma and the objective variable of the model Mb. The distanced_(y) is calculated, for example, in the same way as in the abovecalculation of the distance d_(MZ)-_(y).

The distance d_(v) is a distance in factor values between the factor ofthe model Ma and the factor of the model Mb having the same valueattribute. The distance d_(v) between the factors having the same factorvalue of the model Ma and the model Mb is calculated, for example, asfollows. The distance calculating part 8 first calculates the distancebetween factor values with respect to all the combinations (ka×kbcombinations) between the ka factors having the corresponding factorvalue in the model Ma and kb factors having the corresponding factorvalue in the model Mb. The distance between factor values is calculated,for example, by the processing of the distance calculating part 8 shownin FIG. 23. Among the calculated ka×kb combinations, the combinationhaving a smallest distance between factor values is selected, and thedistance between factor values in that combination is defined as thedistance d_(v). The distance between factor values in a plurality ofcombinations may be selected as the distance d_(v), and in this case,for example, the total of the distances between factor values in theselected plurality of combinations is used for calculating the distancebetween models in Op803.

The distance d_(x) is a value indicating the similarity between theexplanatory variables X₁ to X_(n) of the model Ma and the explanatoryvariables X₁ to X_(n) of the model Mb respectively corresponding to theexplanatory variables X₁ to X_(n), and for example, is calculated in thesame way as in the above distance d_(MZ)-_(x). The distance d_(B)between weights is a value indicating the similarity between the weightsβ₁ to β_(n) of the explanatory variables X₁ to X_(n) of the model Ma andthe weights β₁ to β_(n) of the explanatory variables X₁ to X_(n) of themodel Mb respectively corresponding to the weights β₁ to β_(n) of theexplanatory variables X₁ to X_(n) of the model Ma. The distance d_(B)between weights is, for example, calculated in the same way as in theabove distance d_(MZ)-_(B).

A distance d_(at) between analysis application periods and a distanced_(pt) between predication application periods are values indicating therespective similarities in an analysis application period and aprediction application period between the models Ma and Mb, and can becalculated in the same way as in the above distance d_(MZ)-_(at). Adistance d_(aq) of a fitting degree of analysis-estimation and adistance d_(pq) of a fitting degree of prediction result are valuesindicating the respective similarities in a fitting degree ofanalysis-estimation and a fitting degree of prediction result betweenthe models Ma and Mb, and is calculated in the same way as in the abovedistance d_(MZ)-_(aq).

The distance calculating part 8 obtains the distance d_(m) between themodels Ma and Mb by substituting the distances d_(y), d_(x), d_(B),d_(at), d_(pt), d_(aq), and d_(pq) into a previously incorporatedfunction g ( ) (Op803). Although the function g ( ) is not particularlylimited, it can be represented by the following Expression (14), as anexample. In the following Expressions (14) and (15), K_(y), K_(x),K_(B), K_(at), K_(pt), K_(aq), and K_(pq) are coefficients indicatingthe respective weights of the distances d_(y), d_(x), d_(B), d_(at),d_(pt), d_(aq), and d_(pq).K _(y) ·d _(y) +K _(x) ·d _(x) +K _(B) ·d _(B) +K _(at) ·d _(at) +K_(pt) ·d _(pt) +K _(aq) ·d _(aq) +K _(pq) ·d _(pq)  (14){(K _(y) ·d _(y))²+(K _(x) ·d _(x))²+(K _(B)·d_(B))²+(K _(at) ·d_(at))²+(K _(pt) ·d _(pt))²+(K _(aq) ·d _(aq))²+(K _(pq) ·d_(pq))²}^(1/2)  (15)

Thus, by calculating the distance d_(m) between the models based on thedistance with respect to a plurality of attributes, the similarity ofvarious attributes of the models Ma and Mb can be reflected to thedistance d_(m). Each attribute of the model is not limited to the aboveexample. Furthermore, the distance calculating part 8 is not required tocalculate the distances with respect to all the above attributes, andmay calculate the distances with respect to the attributes whose datacan be obtained.

(Detail of Similar Model Space Creation Processing [Op1605])

FIG. 28 is a flowchart illustrating the detail of the processing inOp1605 in FIG. 24. In the example shown in FIG. 28, the similar modelspace creating part 92 calculates the distance between the objectivevariable of a model represented by each record recorded in the modelinstance DB 63 and the objective variable of the reference model. Thedistance between objective variables can be calculated in the same wayas in the above distance d_(MZ)-_(y). Then, the similar model spacecreating part 92 extracts the instance ID of the model whose distancefrom the objective variable of the reference model is a threshold valueor less as the similar model (Op51).

The similar model space creating part 92 requests the similar factorspace creating part 91 to create similar factor space data with respectto each factor requested to be replaced (factor targeted forreplacement) among the factors of the reference model (Op52). At a timeof providing an instruction, the similar model space creating part 92passes data indicating that the search range of similar factors islimited to the factors included in the model indicated by the instanceID extracted in Op51 as search conditions. Thus, a group of similarfactors having similar factor values are searched for from a group offactors in actually existing similar models with respect to each factorto be replaced, and similar factor space data is generated. The similarfactor space creation processing is performed, for example, as shown inFIG. 22.

The similar model space creating part 92 creates replacement modelsconfigured by replacing the factor to be replaced of the reference modelby a factor included in the group of similar factors indicated by thesimilar factor space data, which have the same factor configuration asthat of the model indicated by the instance ID extracted in Op51 (Op53).

Then, the similar model space creating part 92 calculates the distancesbetween the created replacement models and the reference model, andcreates similar model space data containing information on the group ofreplacement models and the information indicating the distance from thereference model (Op54). Consequently, the model configured by replacingthe factor of the reference model by a factor having a similar value,which has the same factor configuration as that of the actually existingsimilar model, can be extracted as a replacement model. Then, similarmodel space data containing information on the replacement model isgenerated. The similar model space data shown in Table 1 is an exampleof the similar model space data generated by the processing shown inFIG. 28.

(Detail of Similar Model Space Creation Processing [Op1607])

FIG. 29 is a flowchart illustrating the detail of the processing inOp1607 in FIG. 24. In the example shown in FIG. 29, the similar modelspace creating part 92 requests the similar factor space creating part91 to create similar factor space data with respect to each factorrequested to be replaced (each factor targeted for replacement) amongthe factors of the reference model. Thus, similar factor space dataindicating a group of similar factors having similar factor values isgenerated for each factor to be replaced (Op71).

The similar model space creating part 92 extracts a group of similarfactors whose distances from the factors to be replaced is a thresholdvalue or less for each factor to be replaced among a group of similarfactors indicated by the similar factor space data on the basis of eachfactor to be replaced, and combines them to create a replacement virtualmodel (Op72).

Hereinafter, a specific example will be shown. The case will bedescribed in which the target of the reference model is “number ofsystem troubles in S branch office of E credit association” and thereference model has day factors “beginning of next week”, “rainyseason”, and “Wednesday, Thursday, Friday in winter”, as in the modelrepresented by M1 shown in FIG. 12. Furthermore, the factors to bereplaced are assumed to be “beginning of next week”, “rainy season”, and“Wednesday, Thursday, Friday in winter”.

FIG. 30 is a diagram conceptually showing an example of a group ofsimilar factors whose distances from the factors to be replaced are athreshold value or less. In FIG. 30, factors to be replaced Xa1, Xa2,and Xa3 are connected to a group of replacement factors b11 to b13, b21to b23, and b31 to b33 respectively similar to the factors to bereplaced Xa1, Xa2, and Xa3 by arrows. For example, the group ofreplacement factors b11, b12, b13 similar to the factor to be replacedXa1 “beginning of next week” are respectively factors “Monday, Tuesday”,“Monday”, and “business day at the beginning of next week”. In thiscase, the similar model space creating part 92 creates replacementvirtual models by replacing the factor to be replaced “beginning of nextweek” by any of the replacement factors “Monday, Tuesday”, “Monday”, and“business day at the beginning of next week”, replacing the factor to bereplaced “rainy season” by any of replacement factors “early summer ormidsummer”, “summer solstice or early summer”, and “early July tomid-July”, and further replacing the factor to be replaced “Wednesday,Thursday, Friday in winter” by any of replacement factors “Wednesday,Thursday, Friday in December”, “Thursday and Friday at the end of theyear”, and “Friday at the end and beginning of the year”. In this case,three factor replacements can be performed respectively for threefactors to be replaced, so that 3×3×3=27 replacement virtual models arecreated.

The similar model space creating part 92 performs the processings inOp73 to Op75 shown in FIG. 29 for each of 27 replacement virtual models,thereby calculating the distance from the reference model. In Op73, thesimilar model space creating part 92 requests the distance calculatingpart 8 to calculate an inter-model distance between each model whicheach factor of the replacement virtual models belongs and the referencemodel. An average value of the distance between each model thus obtainedand the reference model is calculated as an inter-model average distanced_(ave).

The similar model space creating part 92 also causes the distancecalculating part 8 to calculate the distance d_(v) between factor valuesof each factor to be replaced of the reference model and therespectively replaced replacement factors (Op74). This calculation isperformed by the processing shown in FIG. 23. In this case, the distanced_(m) between model attributes in Op83 shown in FIG. 23 is obtained bycalculating the distance between the attribute of the reference modeland the attribute of the model which each replacement factor belongs.Thus, the distance between factor values to be calculated becomes avalue considering the relationship between the model which eachreplacement factor belongs and the reference model.

The similar model space creating part 92 calculates an inter-modeldistance d_(m) between the replacement virtual model and the referencemodel by substituting the inter-model average distance d_(ave)calculated in Op74 and the distance d_(v) in factor values between eachfactor to be replaced and each replacement factor calculated in Op75into a predetermined function h(d_(ave), d_(v)) (Op76). Thus, aninter-model distance also incorporating the relationship between eachmodel which each replacement factor belongs and the reference model iscalculated.

The distance between each replacement virtual model and the referencemodel is calculated by the processings in Op73 to Op75. The similarmodel space creating part 92 generates data in which each replacementvirtual model is associated with the distance as similar model spacedata.

As a result of the processing shown in FIG. 29, the distance between thereference model and the replacement virtual model is obtained based onthe distance in factor values between the factor to be replaced(original factor) and the replacement factor, and an inter-modeldistance between the reference model and each model to which eachreplacement factor belongs. Thus, the distance also incorporating thematter regarding what value the factor has with respect to the referencemodel to which degree is obtained. Consequently, information isobtained, which contains a group of replacement factors having commonessin factor values (being compatible) with the original factor and whichis effective for obtaining a replacement model suitable for thereference model.

(Screen Display Example: Condition Setting Screen)

Next, an example of a screen which the model creation support system 1causes the information processing apparatuses 15 a to 15 c to displaywill be described. FIG. 31 is an exemplary display of screens which thecondition acquiring part 4 causes the information processing apparatus15 a that has made a request for creating a replacement model of anexisting model to display so as to request information indicating theconditions of the replacement model. The screens shown in FIG. 31 aredisplayed, for example, on the information processing apparatus 15 abefore the model creation processing shown in FIG. 7. The informationinput through the screens is passed to the model proposing part 11through the condition acquiring part 4 as model condition data.

A search condition setting screen 1 (G3) shown in FIG. 31 includes asearch procedure selecting area A1, a fitting degree kind selecting areaA2, a replacement target factor selecting area A3, and a priority periodspecifying area A4.

In the search procedure selecting area A1, a user can select a procedurefor searching for a replacement model. Herein, the user can make theselection between step replacement and batch replacement and theselection between factor-based replacement and model-based replacement.In the case of the model-based replacement, the selection among thelimitation to actual models, the limitation to virtual models, and themixing of actual/virtual models can be made.

The step replacement is a search procedure for presenting replacementmodels or replacement factors one at a time so that the user can select,and the batch replacement is a search procedure for presenting all thereplacement models or replacement factors at once. The selection resultis used, for example, when the model proposing part 11 determineswhether to present a group of similar factors or a group of similarmodels one at a time or to present them at once in Op14 or Op17 shown inFIG. 7.

The selection between the factor-based replacement and the model-basedreplacement is the selection of whether replacement targets are searchedfor on a factor basis or on a model basis, and the selection result isused, for example, for the determination in Op12 shown in FIG. 7. Theselection among the limitation to an actual model, the limitation to avirtual model, and the mixing of actual/virtual models is the selectionof a search target of a replacement model, and the selection resultthereof is used, for example, for the determination in Op1604 and Op1606shown in FIG. 24.

In the fitting degree kind selecting area A2, the user selects which ofthe fitting degree of analysis-estimation and the fitting degree ofprediction result should be taken more seriously in a search for areplacement model or a replacement factor. The selection result is used,for example, for calculating the similarity of a fitting degree ofprediction result or a fitting degree of analysis-estimation in thecalculation of the distance d_(m) between model attributes in thecalculation of a distance between factor values shown in FIG. 23 (Op83)and the calculation of the distance between respective attributes of amodel in the calculation of an inter-model distance shown in FIG. 26.

In the replacement target factor selecting area A3, the user can selecta factor desired to be replaced (factor targeted for replacement) in anexisting model. The information on the factor to be replaced, selectedherein, is passed by the model proposing part 11 to the similar factorspace creating part 91 or the similar model space creating part 92together with a request for processing, for example, in Op13 and Op16shown in FIG. 7. Then, the information is used in the processing in thesimilar factor space creating part 91 or the similar model spacecreating part 92.

In the priority period specifying area A4, the user can select whetheror not to acquire an analysis model always targeting latest informationfrom the information processing apparatus. Furthermore, the user canalso specify the length of an analysis application period to be a targetof an analysis model. For example, the case where “10 days” is input asin an example shown in FIG. 31 means the following: the informationprocessing apparatus conducts an analysis using the data in the past 10days with the past 10 days as an analysis application period (i.e.,generates a predicted model), and predicts the fluctuation of tomorrow,using that model. In this case, for example, the model informationacquiring part 3 receives the generated predicted model, analysisapplication period (past 10 days), fitting degree ofanalysis-estimation, fitting degree of prediction result of the previousday, and the like, and the processing in which the model managing part 5reflects these pieces of information to the data in the model recordingpart 6 may be repeated every day.

The search condition setting screen 2 (G4) is used for the user toselect attributes of factor values to be taken seriously in a search fora replacement model or a replacement factor. In this screen, the usercan select attributes of factor values to be taken seriously among“fitting degree enhancement property by a change in a factor”, “fittingdegree enhancement property by a change in an application period”,“long-term stability”, and “commoness”. The attributes of the factorvalues selected herein are received, for example, by the similar factorspace creating part 91 as search conditions in Op1401 shown in FIG. 22.Then, the similar factor space creating part 91 can narrow down similarfactors to be replacement candidates to a group of factors having factorvalues of the attributes in Op1404.

For example, as shown in FIG. 31, an example of the case will bedescribed in which “long-term stability is taken seriously” is selected,and “from the present time to ‘one year’ ago” is input as a period to betaken seriously on the search condition setting screen 2 (G4). In thiscase, in Op1404 shown in FIG. 22, the similar factor space creating part91 refers to data “applicable period (D3 in FIG. 5)” in the factor valueDB 65 of each of a group of factors taken in Op1403, and define onlyfactors, in which at least a part of “the applicable period” is includedin a period from the present time to one year ago, to be similar factorsto be candidates for replacement factors. As a result, the factorsindicating long-term stability in an achievement from the present timeto one year ago are extracted as candidates for replacement factors.

As shown in the above embodiment, the factor values are classified intoa plurality of attributes from various points of view and recorded inthe factor value DB 65, whereby such a user's selection is enabled.Consequently, replacement models or replacement factors reflecting theuser's intension can be presented.

(Screen Display Example: Screen of Proposing Replacement Factors)

Hereinafter, a transition example of a screen which the conditionacquiring part 4 and the model proposing part 11 cause the informationprocessing apparatus 15 that has made a request for creating replacementmodels of an existing model to display so as to present factors to bereplaced. As an example, the case will be described in which the modelrepresented by Ml shown in FIG. 12 is notified as the existing model,and a request for creating replacement models of that model is made.

In this case, the condition acquiring part 4 inquires about modelcondition data with respect to the information processing apparatus 15a. Specifically, the condition acquiring part 4 causes the informationprocessing apparatus 15 a to display the search condition setting screen1 (G3) and the search condition setting screen 2 (G4) shown in FIG. 31,for example. The information input on these screens is passed to themodel proposing part 11 so as to be included in the model condition datatogether with the information on the existing model. This processingcorresponds to Op11 shown in FIG. 7.

The model proposing part 11 searches the factor value DB 65 forinformation on factor values of the respective factors X1 “beginning ofnext week”, X2 “rainy season”, and X3 “Wednesday, Thursday, Friday inwinter” of the existing model, and passes the information to thecondition acquiring part 4. The condition acquiring part 4 sends theinformation on the factor values of the respective factors to theinformation processing apparatus 15 a, and causes the informationprocessing apparatus 15 a to display the information to the user.

The screen G5 shown in FIG. 32 is an exemplary screen which thecondition acquiring part 4 causes the information processing apparatus15 a to display at this time. On the screen G5, factor value attributes“field commoness”, “long-term stability”, and “enhancement property” ofthe respective factors X1, X2, and X3 of the existing model aredisplayed respectively, and furthermore, an “OK” button for confirmingthat these factors are to be replaced is displayed. When the user clickson the “OK” button, the information indicating that X1, X2, and X3 arefactors to be replaced is sent to the condition acquiring part 4, andfurther passed to the model proposing part 11.

The model proposing part 11 performs the processing shown in FIG. 7 inaccordance with the search conditions input on the screens G3 and G4shown in FIG. 31. Herein, the case where “step replacement” and“factor-based replacement” are selected in the search procedureselecting area Al on the search condition setting screen 1 (G3) will bedescribed. In this case, the model proposing part 11 determines that thesearch procedure is conducted by factor-based replacement (“on a factorbasis” in Op12 shown in FIG. 7), and performs the processings in Op13 toOp15.

At this time, in the presentation of similar factors in Op14, similarfactors that are candidates for replacement are presented to the user onthe basis of a factor to be replaced so that the user can select thesimilar factors. Screens G6 and G7 shown in FIG. 32 are respectivelyexemplary screens presenting similar factors of the factors to bereplaced X3 “Wednesday, Thursday, Friday in winter” and X2 “rainyseason”. The screen G6 presents a similar factor “Thursday and Friday atthe end of the year” of the factor to be replaced X3 “Wednesday,Thursday, Friday in winter”, and the explanations of the factor values“enhancement property” and “field commoness” of the similar factor arealso displayed. When the user clicks on the “OK” button, the modelproposing part 11 creates a replacement model obtained by replacing thefactor to be replaced X1 “Wednesday, Thursday, Friday in winter” by“Thursday and Friday at the end of the year”. When a “NO” button isclicked on, the model proposing part 11 displays another similar factorsimilar to the factor X3 “Wednesday, Thursday, Friday in winter”. Thescreen G6 presents a similar factor “business days in a rainy season” ofthe factor to be replaced X2 “rainy season” together with factor valueinformation. The screens G5 and G6 are successively displayed on theinformation processing apparatus 15 a, whereby the user can determinereplacement factors while considering factor values on a basis of afactor to be replaced.

When the replacement factors are determined with respect to therespective factors to be replaced X1, X2, and X3, the model proposingpart 11 creates models replaced by these replacement factors and sendsthe models to the information processing apparatus 15 a as thereplacement models.

(New Model Creation Processing)

An operation example in the case where the condition acquiring part 4receives a request for creating replacement models of an existing modelhas been mainly described. Hereinafter, an operation example of thecondition acquiring part 4 and the model proposing part 11 will bedescribed in the case where the condition acquiring part 4 receives arequest for creating a new model from the information processingapparatus 15 a.

FIG. 33 is a flowchart illustrating an example of new model creationprocessing of the condition acquiring part 4 and the model proposingpart 11. In the example shown in FIG. 33, when receiving a request forcreating a new model from the information processing apparatus 15 a(Op21), the condition acquiring part 4 notifies the model proposing part11 of a request for creating a new model. The model proposing part 11acquires all the types of categories (fields) and kinds of a phenomenonto be a target of a model recorded in the objective variable DB 61,sends them to the information processing apparatus 15 a, and causes theinformation processing apparatus 15 a to present them to the user sothat the user can select them (Op22).

A screen G8 shown in FIG. 34 is an exemplary screen in the case wherethe categories (fields) of a phenomenon and the kinds thereof aredisplayed so as to be selected. The categories (fields) of thephenomenon acquired by the model proposing part 11 from the objectivevariable DB 61 are displayed in a list L1 of “field selection”, and thekinds of the phenomenon are displayed in a list L2 of “objectivevariable (Y)”, respectively.

The category (field) and the kind of the phenomenon selected by the useron the screen G8 are notified to the model proposing part 11. The modelproposing part 11 acquires factors contributing to a model targeting thenotified phenomenon from the factor value DB 65, and present them sothat the user can select them (Op23 shown in FIG. 33). Specifically, themodel proposing part 11 acquires an objective variable ID of theobjective variable including both the selected category (field) and kindof the phenomenon from the objective variable DB 61 (see FIG. 3). Then,the model proposing part 11 acquires a record including the acquiredobjective variable ID from the model instance DB 63. Information on thecontributing factors (explanatory variable IDs) indicated by theacquired record is sent to the information processing apparatus 15 a anddisplayed so that the user can select the information.

A screen G9 shown in FIG. 34 is an exemplary screen displaying thefactors extracted by the model proposing part 11 in a selectable list. Alist L3 on the screen G9 displays names of the extracted factors so thatthey can be selected. The user selects at least one factor desired to beadded to the model from the list L3. The model proposing part 11 isnotified of the factor selected by the user. The model proposing part 11generates a regression equation of the model including the selectedfactor and sends it to the information processing apparatus 15 a (Op24shown in FIG. 33).

Thus, the information processing apparatus 15 a can obtain a new model.The above new model creation processing is an example and is not limitedthereto. For example, the model proposing part 11 may further narrowdown the factors extracted from the factor value DB 65 based on anumerical value indicating the degree of a factor value such asenhancement performance or an average fitting degree and determine afactor to be included in the model, instead of allowing the user toselect a factor to be included in the model in Op23. This makes theuser's selection operation unnecessary.

Embodiment 2

In the present embodiment, the case will be described where a modeldealt with by the model creation support system 1 contains informationindicating the relationship between explanatory variables in aregression equation. More specifically, the case where a model containsinformation indicating the relationship between factors in one modelwill be described.

FIG. 35 shows a path chart P1 illustrating the relationship betweenvariables in the model represented by M1 shown in FIG. 12, and anequation H1 representing the relationship in the path chart, using amatrix. In the path chart P1, arrows in one direction representcause-and-effect relationships, and values (β₀ to β₃) on the sides ofthe arrows represent weights (parameters). It is understood that eachobjective variable Y (the number of system troubles of a creditassociation) is influenced by the respective explanatory variables X₀ toX₃ (a constant term, beginning of next week, rainy season, Wednesday,Thursday, Friday in winter). However, the relationship between theexplanatory variables X₀ to X₃ is not shown. Therefore, in the equationH1 representing the relationship of the path chart P1, the first tofourth rows in the matrix do not substantially show anything.

FIG. 36 shows a path chart P2 including the relationship between theexplanatory variables X₀ to X₃, and an equation H2 representing therelationship in the path chart, using a matrix. The path chart P2 has aconfiguration in which the relationship between the explanatoryvariables X₀ to X₃ is added to the relationship represented by the pathchart P1 in FIG. 35. In the path chart P2, arrows in both directionsrepresent correlations. Specifically, the explanatory variables X₁ andX₂ have correlations. The weight (coefficient) representing thecorrelation degree between X₁ and X₂ is a₁₂. Similarly, the explanatoryvariables X₂ and X₃ and the explanatory variables X₁ and X₃ also havecorrelations with weights (coefficients) a₂₃ and a₁₃.

The relationship represented by the path chart P2 is represented by theequation H2. In this case, the relationships between X₁ and X₂, X₂ andX₃, and X₁ and X₃ are in both directions (have correlations) instead ofone direction. Therefore, the coefficients a₁₂, a₂₃, and a₁₃representing the degrees of these relationships are described at twopositions on both sides with respect to a diagonal line in a matrix K2.In the case where the relationship between X₁ and X₂ is in one directionfrom X₂ to X₁ (in the case where X₁ does not influence X2 although X₂influences X₁), an element on the third row and second column in thematrix K2 becomes “0”.

The equation H2 can be dealt with as a covariance structure equation.Therefore, for example, each coefficient in the matrix K2 is calculatedusing the procedure of a covariance structure analysis.

The data indicating the relationship (link) between explanatoryvariables is recorded in the model instance DB 63 as an inter-factorlink and the weight of a link, for example. FIG. 37 shows an example ofa record storing an inter-factor link and the weight of a link in themodel instance DB 63. In a record A3 shown in FIG. 37, “X1-X2, X2-X1” isrecorded as the inter-factor link. This is data indicating that theexplanatory variable X1 influences the explanatory variable X2, and theexplanatory variable X2 influences the explanatory variable X1. Morespecifically, this data indicates that there is a correlation betweenthe factor of the explanatory variable X1 and the factor of theexplanatory variable X2. Furthermore, as data indicating the weight ofeach link of the inter-factor link “X1-X2, X2-X1”, “a₁₂, a₁₂” isrecorded.

Next, exemplary calculations of the distances between factor values andbetween models by the distance calculating part 8 will be described inthe case where information indicating the relationship between factorsis included in a regression equation. First, the case of calculating thedistance between factor values will be described. The distancecalculating part 8 calculates the distance d_(m) between modelattributes in Op83 in the calculation of the distance between factorvalues shown in FIG. 23. At this time, in the case where both the modelscontain information indicating the relationship between factors, thedistance calculating part 8 can further calculate the distance d_(MZ-s)between links and the distance d_(MZ-n) between weights of the link asone distance d_(m).

The distance d_(MZ-s) between links is a numerical value indicating thesimilarity between the relationship (link) between factors in one modeland the relationship (link) between factors in the other model. Thedistance d_(MZ-s) between links is calculated, for example, based ondata on an inter-factor link recorded so as to be associated withrecords of both the models in the model instance DB 63. For example, thedistance d_(MZ-s) between links is calculated by the followingExpression (16).

$\begin{matrix}{d_{{MZ} - s} = \frac{{{Number}\mspace{14mu}{of}\mspace{14mu}{links}\mspace{14mu}{of}{\mspace{11mu}\;}A}\bigcap B}{{{Number}\mspace{14mu}{of}\mspace{14mu}{links}\mspace{14mu}{of}\mspace{14mu} A}\bigcup B}} & (16)\end{matrix}$

In Expression (16), the “number of links of A∩B” represents the totalnumber of links common to a model A and a model B, and the “number oflinks of A∪B” represents the total number of links included in at leastone of the model A and the model B.

The distance d_(MZ-n) between weights of links is a numerical valuerepresenting the similarity of the weights of the links. The distanced_(MZ-n) between weights of links is also calculated based on theweights of the links of records corresponding to both the models in themodel instance DB 63. The distance d_(MZ-n) is calculated by thefollowing Expression (17).

$\begin{matrix}{d_{{MZ} - n} = {1 - {\frac{\min\left( {\left\lceil {{Weight}\mspace{14mu}{of}\mspace{14mu}{link}\mspace{14mu} A} \right\rfloor,\left\lceil {{Weight}\mspace{14mu}{of}\mspace{14mu}{link}\mspace{14mu} B} \right\rfloor} \right)}{\max\left( {\left\lceil {{Weight}\mspace{14mu}{of}\mspace{14mu}{link}\mspace{14mu} A} \right\rfloor,\left\lceil {{Weight}\mspace{14mu}{of}\mspace{14mu}{link}\mspace{14mu} B} \right\rfloor} \right)}}}} & (17)\end{matrix}$

In Op84 shown in FIG. 23, the distance d_(v) between factor values iscalculated using the distance d_(m) including the distance d_(MZ-s) andthe distance d_(MZ-n). Thus, the distance d_(v) between factor valuesincorporating the distance between links and the distance betweenweights of links is calculated.

Even in the case of calculating the distance d_(m) between models,similarly, the distance calculating part 8 can further calculate thedistance d_(MZ-s) between links and the distance d_(MZ-n) betweenweights of links, when calculating the distance between model attributes(see Op802 in FIG. 26). Then, in Op803, the distance calculating part 8substitutes the distance d_(MZ-s) and the distance d_(MZ-n) into thefunction g( ), thereby calculating the distance d_(m) between models, inaddition to the distance between the other attributes. Thus, thedistance d_(m) between models incorporating the distance between linksand the distance between weights of links is calculated.

In the above, an example in which the distance calculating part 8calculates both the distance d_(MZ-s) between links and the distanced_(MZ-n) between weights of links has been described. However, thedistance calculating part 8 may calculate only one of the distanced_(MZ-s) and the distance d_(MZ-n), and may calculate a synthetic valueof the distance d_(MZ-s) and the distance d_(MZ-n).

Embodiment 3

FIG. 38 is a functional block diagram showing a configuration of a modelcreation support system according to Embodiment 3. A model creationsupport system 10 shown in FIG. 38 has a configuration in which an eventfactor creating part 12 and a factor procuring part 13 are added to themodel creation support system 1 shown in FIG. 1. In the model recordingpart 6 a, a factor relation information DB is further recorded. In thefactor relation information DB, for example, event informationindicating the relationship between the characteristics of an event andthe factors is recorded.

FIG. 39 is a diagram showing an example of data contents to be recordedas event information. A record E1 shown in FIG. 39 is data in which dataindicating an event name, a period, a place, an institution, and a fieldare recorded so as to be associated with an event ID. The contents ofthe event information are not limited to the example shown in FIG. 39.

(Description of the Event Factor Creating Part 12)

In the present embodiment, the model managing part 5 a instructs theevent factor creating part 12 to create an event factor. For example, inthe case of receiving a request for creating a replacement model fromthe information processing apparatuses 15 a, 15 b, or 15 c, and furtherreceiving a request for creating an event factor, the model managingpart 5 a provides an instruction to the event factor creating part 12.Furthermore, the model managing part 5 a may provide, for example, aninstruction of creating an event factor periodically as nighttime batchprocessing.

The event factor creating part 12 compares the information on aparticular factor recorded in the explanatory variable DB 62 with eventinformation recorded in the factor relation information DB in accordancewith the instruction from the model managing part 5 a, therebydetermining whether or not there is an event corresponding to theparticular factor. If there is an event corresponding to the factor, theevent factor creating part 12 creates data indicating day factors(hereinafter, referred to as event factors) of the event. At this time,the data recorded on the explanatory variable DB 62 and the factor valueDB 65 is used for creating event factors. Furthermore, the created datais recorded in the explanatory variable DB 62.

FIG. 40 is a flowchart illustrating an operation example of the eventfactor creating part 12. In the example shown in FIG. 40, when receivingan instruction from the model managing part 5 (Op901), the event factorcreating part 12 determines whether or not the instruction is forsearching for an event factor corresponding to a particular factorincluded in a particular model (Op902). For example, in the case wherethe instruction from the model managing part 5 contains data indicatinga particular model and factor, the event factor creating part 12 candetermine that the instruction is for searching for an event factorcorresponding to the particular factor.

For example, in the case of receiving a request for searching for anevent factor corresponding to a particular model and factor from themodel proposing part 11, the model managing part 5 can instruct theevent factor creating part 12 to search for and create an event factorcorresponding to the particular event and factor. On the other hand, forexample, in the case where the model managing part 5 instructs the evenfactor creating part 12 to create an event factor as nighttime batchprocessing, data indicating the particular model and factor is notcontained in the instruction.

In the case of Yes (Y) in Op902, the event factor creating part 12collects a group of similar models having factors similar to those ofthe particular model from the model instance DB 63 (Op903). At thistime, the event factor creating part 12 extracts a model having factors(similar factors) similar to the particular factor among models havingthe same category (region) or category (institution) of a targetphenomenon of the particular model. For example, the distance betweenfactor values of two factors is calculated by the processing in thedistance calculating part 8 shown in FIG. 23, and the determination ofthe comparison between the two factors can be determined based on thedistance.

The event factor creating part 12 determines whether or not eventinformation corresponding to similar factors of the extracted group ofsimilar models is recorded as event information of the factor relationinformation DB (Op904). The event factor creating part 12 compares, forexample, a period specified by the similar factors with an event period.Specifically, in the case where the similar factors are day factors, aday specified by the similar factors is compared with a period of eachevent recorded in event information. When the degree to which the dayspecified by the similar factors is matched with the period of eachevent satisfies predetermined conditions, the event factor creating part12 can determine that the event corresponds to the similar factors.

If there is an event corresponding to the similar factors, the eventfactor creating part 12 creates data on the event factor representingthe event (Op905). The data on the event factor is, for example,represented by a matrix of one column and 365 rows with each day in oneyear from January 1 to December 31 as an element, as represented by thefollowing Expression (18).

$\begin{matrix}{X_{e} = {\begin{matrix}{\text{1}\text{/}\text{1}} \\\vdots \\{\text{10}\text{/}\text{6}} \\{\text{10}\text{/}\text{7}} \\{\text{10}\text{/}\text{8}} \\{\text{10}\text{/}\text{9}} \\{\text{10}\text{/}\text{10}} \\\vdots \\{\text{12}\text{/}\text{31}}\end{matrix}\begin{pmatrix}\text{0} \\\vdots \\\text{0} \\\text{1} \\\text{1} \\\text{1} \\\text{0} \\\vdots \\\text{0}\end{pmatrix}}} & (18)\end{matrix}$

The data on the event factor is, for example, sent to the model managingpart 5. The model managing part 5 records information such as the dataon the event factor, an event name, and an application range in theexplanatory variable DB 62 so that they are associated with theexplanatory variable ID.

At this time, the event factor creating part 12 may create factor valuedata on the event factor. There is a high possibility that the eventfactor created in Op905 has a factor value similar to that of thesimilar factor, so that the event factor creating part 12 can generatefactor value data on the event factor, for example, using factor valuedata on the similar factor. The factor value data on the event factor isrecorded in the factor value DB. Consequently, the event factor is alsoincluded in the search range of the similar factor space creating part91. The applicable range of the event factor is limited in most cases.(For example, the influence of a festival in a shrine is limited to thevicinity of cities, towns and villages, and the effect of a sales eventin a supermarket is limited to the catchment area thereof. Therefore,the similar factor space creating part 91 may determine whether or notthe applicable range of the event factor recorded in the explanatoryvariable DB 62 includes a model to which the reference factor belongs,in searching for factors. In the case of No(N) in Op902, the eventfactor creating part 12 determines a particular region and institutionto be searched for. Then, the event factor creating part 12 extracts agroup of models targeting the phenomenon of the determined region andinstitution from the model instance DB 63 (Op907). Specifically, theevent factor creating part 12 may extract objective variables at whichthe category (region) and the category (institution) are matched withthe determined region and institution from the objective variable DB 61,and extract a record including the extracted objective variables fromthe model instance DB 63. Consequently, a group of models targeting thephenomenon of the region and institution are extracted.

Herein, as an example, models having a region and an institution incommon are extracted; however, the conditions for extracting models arenot limited thereto. For example, models having the same kind of regionand phenomenon may be extracted.

The event factor creating part 12 determines whether or not eventinformation corresponding to a factor included commonly in the extractedgroup of models is recorded as event information of the factor relationinformation DB (Op907). Herein, the processing of specifying the factorincluded commonly in the extracted group of models can be performed inthe same way as in Op642 shown in FIG. 20.

In the case of Yes (Y) in Op907, the event factor creating part 12creates data on the event factor indicating the event (Op908). Thecreated data on the event factor is recorded in the explanatory variableDB 62. Furthermore, the event factor creating part 12 may also createfactor value data on the event factor at this time.

The processings in Op901 to Op908 are repeated unless a stop instructionis provided (as long as N is obtained in Op909). Consequently, everytime the model managing part 5 provides an instruction, an event factoris created in accordance with the instruction, and the data is recordedin the explanatory variable DB 62.

(Description of the Factor Procuring Part 13)

The factor procuring part 13 receives the designation of day factorsfrom the model managing part 5 a, and searches the explanatory variableB for more appropriate modified day species and outputs them as dayspecies (hereinafter, referred to as designated day species) indicatedby the designated day factors. For example, the factor procuring part 13measures the existence criterion on a time coordinate of the designatedday species, and searches the explanatory variable DB 62 for moreappropriate day species as the designated day species based on theexistence criterion. The existence criterion on the time coordinateincludes, for example, a relative distance indicating the overlappingdegree and adjacency between the period indicated by a day species andthe period indicated by the designated day species, an appearancefrequency in a predetermined period of the period indicated by thedesignated day species, and the like.

The factor procuring part 13 can calculate the above relative distance,for example, by comparing an element value of the designated day specieswith an element value of each day species recorded on the element valuetable of the explanatory variable DB 62. Furthermore, the factorprocuring part 13 can calculate the above appearance frequency bycalculating the scattering degree of a value “1” in the element valuesof the designated day species.

Next, an operation example of the factor procuring part 13 will bedescribed. The model proposing part 11, for example, requests the modelmanaging part 5 a to modify a factors to be replaced, designated by theinformation processing apparatuses 15 a to 15 c, or a factor extractedas a replacement factor by the model proposing part 11. Herein, anoperation example of the factor procuring part 13 in the case ofreceiving a request for modifying a particular day factor from the modelproposing part 11 will be described with reference to FIG. 41.

In the example shown in FIG. 41, first, the factor procuring part 13receives the designation of a day factor requested to be modified fromthe model managing part 5 a (Op701). At this time, the factor procuringpart 13 receives, for example, data on each element value of the dayspecies (designated day species) indicated by the day factor, and dataindicating the presence/absence of a request for creating a complex dayspecies from the model managing part 5 a. The factor procuring part 13may receive the name of the designated day species instead of the dataon each element value, and acquire each element value data on thedesignated day species, referring to the explanatory variable DB 62.

The factor procuring part 13 extracts a day species having highadjacency, similarity, or inclusiveness with respect to the designatedday species (Op702). For example, the factor procuring part 13 compareseach element value of the designated factor with the element value (seea record C100 in FIG. 4) of each day factor recorded in the explanatoryvariable DB 62, thereby determining the presence/absence of adjacency,similarity, and inclusiveness. Herein, referring to FIG. 42, a specificexample of the determination processing will be described. The followingdetermination processing is an example and is not limited thereto.

FIG. 42 shows an example of element values of day factors “GW (goldenweek)”, “late spring”, “beginning of summer” and “late spring tobeginning of summer”. The example shown in FIG. 42 is an example in2004, and in the element values of “GW”, “1” is set from April 29 to May5, and “0” is set in the remaining days. In the element values of “latespring”, “1” is set from April 14 to April 26. In the element values of“beginning of summer”, “1” is set from April 30 to May 12. In theelement values of “late spring to beginning of spring”, “1” is set fromApril 14 to May 12. Day species representing 24 seasonal datum points(season sections in the lunar calendar) such as “late spring” and“beginning of summer” can be defined as a period of 6 days (13 days intotal) before and after the applicable day (April 20 in the case of“late spring” and May 6 in the case of “beginning of summer”) as in theexample shown in FIG. 42.

The factor procuring part 13 specifies a period in which an elementvalue “1” continues, with reference to element values of each dayfactor. The factor procuring part 13 compares the period in which theelement value “1” continues between the day factors and determines thepresence/absence of adjacency, similarity, and inclusiveness. Forexample, a period in which “1” continues in the “GW” and a period inwhich “1” continues in the “late spring” shown in FIG. 42 are adjacentto each other with two days (April 27 and April 28) interposedtherebetween. In this case, the factor procuring part 13 can determinethat there is adjacency between the “GW” and the “late spring”. Thus,the adjacency can be determined based on the number of days interposedbetween the periods in which “1” continues.

Furthermore, the period in which “1” continues in the “GW” and theperiod in which “1” continues in the “late spring” overlap each otherfor 6 days (April 30 to May 5). In this case, the factor procuring part13 can determine that there is similarity between the “GW” and the“beginning of summer”. Thus, the similarity can be determined based onthe number of overlapping days between the periods in which “1”continues.

Furthermore, the period (April 29 to May 5) in which “1” continues inthe “GW” is completely included in the period (April 14 to May 12) inwhich “1” continues in the “later spring to beginning of summer”. Inthis case, the factor procuring part 13 can determine that the “GW” isincluded in the “late spring to beginning of summer” (there isinclusiveness). Thus, the presence/absence of the inclusiveness may bedetermined based on whether or not the period in which “1” continues inone day species completely includes the period in which “1” continues inthe other day species.

In the case where Yes(Y) is determined in Op702, it is determinedwhether or not there is a request for creating a complex day speciesbased on the data received in Op701 (Op703). In the case where Yes(Y) isdetermined in Op703, the factor procuring part 13 creates day speciesdata obtained by an OR and an AND of the extracted day species and thedesignated day species as a complex day factor and returns the dayspecies data to the model managing part 5 a. Furthermore, the factorprocuring part 13 can record the created complex day factor in theexplanatory variable DB 62.

Furthermore, the factor procuring part 13 may create factor value dataon the created complex day factor and record the factor value data inthe factor value DB 65. There is a high possibility that the complex dayfactor created in Op704 has a factor value similar to that of thedesignated day factor, so that the factor procuring part 13 can generatefactor value data on the complex day factor, for example, using thefactor value data on the designated day factor. Thus, the complex dayfactor created in Op704 can also be included in the search range of thesimilar factor space creating part 91.

In the case where No(N) is determined in Op703, the factor procuringpart 13 creates day species data on the extracted day species in Op702,and returns the day species data to the model managing part 51 as a newday factor. The new day factor may also be recorded in the explanatoryvariable DB 62 in the same way as in the complex day factor.Furthermore, the factor procuring part 13 may generate factor value dataon the new day factor and record the factor value data in the factorvalue DB 65.

On the other hand, in the case where No(N) is determined in Op702, i.e.,in the case where day species having high adjacency, similarity, orinclusiveness with respect to the designated day species is notextracted, the factor procuring part 13 determines whether or not thereis a period (uneven distribution period) in which the appearancefrequency of a day specified by the designated day species is higherthan that in the other periods (Op706). For example, processing in thecase where the designated day species is “weekend and luckiest day” willbe described. The following Table 5 shows days which have an elementvalue “1” in one year of 2004.

TABLE 5 January 4 February 1 February 7 February 29 March 6 April 11April 17 May 9 May 15 June 13 August 15 October 16 November 13 December12 December 18

In the example shown in Table 5, there are 10 days in the first half(January to June) in 2004 during which the element value of “weekend andluckiest day” is “1”, and there are 5 days in the second half (August toDecember). Thus, the days during which the element value of “weekend andluckiest day” is “1” are concentrated in the first half. In this case,the factor procuring part 13 detects the first half in 2004 as an unevendistribution period.

In the case where an uneven distribution period of the designated dayspecies is extracted in Op706, the factor procuring part 13 creates aday factor in which the designated day species are narrowed down to theuneven distribution period and returns the day factor to the modelmanaging part 5 a (Op707). In the case of the above example “weekend andluckiest day” a day factor narrowed down to the first half of a year iscreated. The element value of the day factor can be set to be an AND ofthe element value of the “first half of a year” and the element value ofthe “weekend and luckiest day” (“first half of a year” AND “weekend andluckiest day”). The day factor created in Op707 may also be recorded inthe explanatory variable DB 62 in the same way as in the complex dayfactor. Furthermore, the factor procuring part 13 may generate factorvalue data on the created day factor and record the factor value data inthe factor value DB 65.

As described above, as a result of the processing of the factorprocuring part 13 shown in FIG. 41, the appropriate day species that isto replace the designated day species is extracted automatically. Thus,the processing of extracting appropriate factors, which used to dependupon the experiment and hunch of an expertise conventionally, can beperformed automatically. The model managing part 5 a returns the dataindicating the day factor that is to replace the designated day speciesto the model proposing part 11. The model proposing part 11 can send,for example, data indicating a day factor to the information processingapparatuses 15 a to 15 c as one of replacement factor candidates of anexisting model. The processing of the factor procuring part 13 may beperformed when the model proposing part 11 extracts replacement factorsof the factor of the existing model or may be performed as nighttimebatch processing, as described in the above example.

In the present embodiment, the processing of creating a day factor hasbeen described. However, the factor created by the factor procuring part13 is not limited to a day factor. A day species is an example of a timespecies, and a time factor other than the day species may be created.More specifically, the time species includes, for example, a time zonerepresented on an hour or minute basis, and the name of a periodrepresented on the basis of a week, month, year, or other units, inaddition to the day species. Examples of the name of a time zoneexpressed on an hour or minute basis include “midnight”, “earlymorning”, “lunch time”, “0 minute per hour”, and “commuting rush-hour”.Furthermore, the factors created by the factor procuring part 13 may bethose of an explanatory variable represented by a matrix or a vectorwith 0 or 1 as an element, without being limited a time species. Forexample, the characteristics factors indicating the characteristicscharacterized by a place, an event, or the like can also be targeted forcreation by the factor procuring part 13.

The embodiments of the present invention have been described. The modelcreation support system of the above embodiments accumulates factors ofa model used in an information processing apparatuses, extracts anappropriate factor fitted for a model requested by the informationprocessing apparatus from the accumulated factors, and outputs thefactors to the information processing apparatus as support data.Therefore, the model creation support system can accumulate informationon the factors of the model and utilize the information for enhancingthe fitting degree of the model.

As in the above embodiments, the model to be a target in the presentinvention is not necessarily expressed by a linear regression equationas in the above embodiments. For example, a model represented by alinear regression equation utilizing a logistic function is alsoincluded in a model to be a target in the present invention.

The present invention is applicable as a model creation support systemenabling the information on factors of a model to be used for predictingvarious phenomena using a computer to be shared and utilized.

The invention may be embodied in other forms without departing from thespirit or essential characteristics thereof. The embodiments disclosedin this application are to be considered in all respects as illustrativeand not limiting. The scope of the invention is indicated by theappended claims rather than by the foregoing description, and allchanges which come within the meaning and range of equivalency of theclaims are intended to be embraced therein.

What is claimed is:
 1. A model creation support system capable ofaccessing an information processing apparatus that predicts or analyzesa phenomenon and a factor contributing to the phenomenon to be a targetfor prediction or analysis, using a model that is data indicating aregression equation, the system comprising: a model managing part thatacquires, from the information processing apparatus, an identifier ofthe model, the regression equation of the model in which the phenomenonis represented by an objective variable and the factor is represented byan explanatory variable, phenomenon data that includes an objectivevariable ID identifying the objective variable of the regressionequation and information indicating the phenomenon represented by theobjective variable, factor data that includes an explanatory variable IDidentifying the explanatory variable and information indicating thefactor represented by the explanatory variable of the regressionequation, and evaluation data containing a fitting degree of the modeland accumulates them in a model recording part accessible from the modelcreation support system; a factor value extracting part that extracts,by referring to the evaluation data on at least one model in the dataaccumulated in the model recording part, an objective variable ID and anexplanatory variable ID identifying an explanatory variable representinga factor that contributes to enhancement of the fitting degree of the atleast one model and records factor value data that associates thephenomenon data of the extracted objective variable ID, the factor dataof the extracted explanatory variable ID and data indicating a degree ofthe contribution in a factor value recording part accessible from themodel creation support system; a condition acquiring part that receivesa request for supporting model creation from the information processingapparatus, and further receives an input of model condition datacontaining data indicating a phenomenon to be a target of a requestedmodel; and a model proposing part that matches the data indicating thephenomenon to be a target of the requested model contained in the modelcondition data with the phenomenon data associated by the factor valuedata in the factor value recording part, thereby extracting arecommended factor capable of contributing to the enhancement of afitting degree of the requested model and outputting support datacontaining data indicating the extracted recommended factor to theinformation processing apparatus.
 2. The model creation support systemaccording to claim 1, wherein the factor value extracting part includes:an inner-model factor value extracting part that detects a transition ofthe factor data specifying the explanatory variable indicating thefactor included in the regression equation of the model regarding atleast one phenomenon and a transition of the evaluation data from thedata accumulated in the model recording part, thereby generating factorvalue data indicating a degree to which a factor indicated by anexplanatory variable added or deleted with respect to the regressionequation of the model contributes to the enhancement of a fitting degreeof the model and records the factor value data in the factor valuerecording part; and an inter-model factor value extracting part thatrefers to factor data specifying explanatory variables indicatingfactors included in regression equations of a plurality of models andevaluation data in the data accumulated in the model recording part,thereby generating factor value data indicating a degree to which thefactors contribute to enhancement of a fitting degree with respect tothe plurality of models and recording the factor value data in thefactor value recording part.
 3. The model creation support systemaccording to claim 2, wherein the inner-model factor value extractingpart that, regarding the factor-indicated by the explanatory variableadded or deleted with respect to the regression equation of the model inthe detected transition of the factor data, detects a change in afitting degree of the model before and after the addition or thedeletion from the evaluation data accumulated in the model recordingpart and generates factor value data on the factor indicated by theexplanatory variable based on a degree of the change.
 4. The modelcreation support system according to claim 2, wherein the model managingpart further acquires application period data indicating an applicationperiod of the model and significance data indicating significance ofeach factor indicated by each explanatory variable included in theregression equation of the model and accumulates the application perioddata and the significance data in the model recording part, and theinner-model factor value extracting part detects a change in theapplication period of the model based on the application period dataaccumulated in the model recording part regarding at least onephenomenon, extracts a factor that contributes to enhancement of afitting degree of the model when the application period changes based ona change in the fitting degree of the model and a change in thesignificance data on each factor in the model before and after thechange in the application period, and generates factor value dataindicating a degree to which the extracted factor contributes to themodel.
 5. The model creation support system according to claim 2,wherein the model managing part further acquires significance dataindicating significance of each factor indicated by each explanatoryvariable included in the regression equation of the model andaccumulates the significance data in the model recording part, and theinter-model factor value extracting part refers to factor dataindicating a group of factors indicated by a group of explanatoryvariables included in each regression equation of a plurality of modelstargeting a particular phenomenon and significance data indicating eachof the group of factors in the data accumulated in the model recordingpart, thereby generating factor value data indicating a contributiondegree of factors that influence the plurality of models commonly. 6.The model creation support system according to claim 2, wherein themodel managing part further acquires application period data indicatingan application period of the model and significance data indicatingsignificance of the factor indicated by the explanatory variable in theregression equation of the model and accumulates the application perioddata and the significance data in the model recording part, and theinter-model factor value extracting part refers to factor dataindicating a group of factors indicated by a group of explanatoryvariables in each regression equation of a plurality of models havingdifferent application periods, which target the same phenomenon, andsignificance data indicating each of the group of factors in the dataaccumulated in the model recording part, thereby generating factor valuedata indicating a contribution degree of the factors with respect to theplurality of models having the different application periods.
 7. Themodel creation support system according to claim 1, wherein thecondition acquiring part acquires a regression equation of an existingmodel that is being used or is to be used in the information processingapparatus and information specifying a target phenomenon of the existingmodel from the information processing apparatus as the model conditiondata, and the model proposing part acquires factor value data associatedwith phenomenon data specifying a objective variable indicating aphenomenon that is the same as or similar to the target phenomenon ofthe existing model among the factors indicated by the factor value datarecorded in the factor value recording part, thereby extracting arecommended factor contributing to enhancement of a fitting degree ofthe existing model, creating a model of a regression equation includingan explanatory variable indicating the extracted recommended factor,including the created model in the support data as a replacement modelof the existing model, and outputting the created model to theinformation processing apparatus.
 8. The model creation support systemaccording to claim 7, wherein the model proposing part compares factorvalue data on a reference factor with factor value data on a pluralityof factors other than the reference factor, using a factor indicated byan explanatory variable included in the regression equation of theexisting model as the reference factor, thereby calculating a similarityof a degree of contribution to the enhancement of a fitting degreebetween the reference factor and each of the other plurality of factors,and extracting a recommended factor capable of contributing to theenhancement of a fitting degree of the existing model to create thereplacement model, based on the similarity.
 9. The model creationsupport system according to claim 1, wherein the condition acquiringpart acquires a regression equation of an existing model that is beingused or is to be used in the information processing apparatus andinformation specifying a target phenomenon of the existing model fromthe information processing apparatus as the model condition data, andthe model proposing part extracts a plurality of replacement modelcandidates to be candidates of the replacement model, calculates asimilarity between the replacement model candidates and the existingmodel, and extracts the replacement model candidate having a relativelyhigh similarity as a replacement model.
 10. The model creation supportsystem according claim 1, wherein the model managing part accumulates,regarding a model represented by a regression equation in which anexplanatory variable is a characteristics factor value representingcharacteristics of a factor by a vector or a matrix using 0 or 1 as anelement, an identifier of a model, phenomenon data specifying aobjective variable indicating a target phenomenon of the model, aregression equation of the model, factor data specifying a explanatoryvariable indicating a factor included in the regression equation, andevaluation data containing a fitting degree of the model in the modelrecording part, the system further comprising: an event informationrecording part that records event information indicating characteristicsof an event; and an event factor creating part that matches the eventinformation with the characteristics factor value that is an explanatoryvariable in the regression equation of the model recorded in the modelrecording part, generates factor data on an event factor indicating thecharacteristics of the event based on the characteristics factor valueif there is the characteristics factor value corresponding to the event,and records the factor data in the model recording part.
 11. The modelcreation support system according to claim 1, wherein the model managingpart accumulates, regarding a model represented by a regression equationin which an explanatory variable is a time factor value representingtime characteristics of a factor by a vector or a matrix using 0 or 1 asan element, an identifier of a model, phenomenon data specifying aobjective variable indicating a target phenomenon of the model, aregression equation of the model, factor data containing a time factorvalue of an explanatory variable included in the regression equation,and evaluation data containing a fitting degree of the model in themodel recording part, the system further comprising a factor procuringpart that acquires designated factor data indicating a designated factorrequested to be modified from the information processing apparatus orthe model proposing part, matches a time factor value of the designatedfactor with a time factor value of the factor data in the modelrecording part, thereby extracting a factor having a predeterminedrelationship with the designated factor from the model recording partand recording factor data represented by a time factor value of theextracted factor or a complex time factor value obtained by an OR or anAND of the time factor value of the extracted factor and a time factorof the designated factor in the model recording part as factor data on amodified factor of the designated factor.
 12. A recording medium storinga model creation support program causing a computer to performprocessing, which is capable of accessing an information processingapparatus that predicts or analyzes a phenomenon and a factorcontributing to the phenomenon to be a target for prediction oranalysis, using a model that is data indicating a regression equation,the model creation support program causing the computer to perform thefollowing: model managing processing of acquiring, from the informationprocessing apparatus, an identifier of the model, the regressionequation of the model in which the phenomenon is represented by anobjective variable and the factor is represented by an explanatoryvariable, phenomenon data that includes an objective variable IDidentifying the objective variable of the regression equation andinformation indicating the phenomenon represented by the objectivevariable, factor data that includes an explanatory variable IDidentifying the explanatory variable and information indicating thefactor represented by the explanatory variable of the regressionequation, and evaluation data containing a fitting degree of the model,and accumulating them in a model recording part accessible from thecomputer; factor value extracting processing of referring to theevaluation data on at least one model in the data accumulated in themodel recording part, thereby extracting an objective variable ID and anexplanatory variable ID identifying an explanatory variable representinga factor that contributes to enhancement of the fitting degree of the atleast one model and recording the factor value data that associates thephenomenon data of the extracted objective variable ID, the factor dataof the extracted explanatory variable ID and data indicating a degree ofthe contribution in a factor value recording part accessible from thecomputer; condition acquiring processing of receiving a request forsupporting model creation from the information processing apparatus, andfurther receiving an input of model condition data containing dataindicating a phenomenon to be a target of a requested model; and modelproposing processing that matches the data indicating the phenomenon tobe a target of the requested model contained in the model condition datawith the phenomenon data associated by the factor value data in thefactor value recording part, thereby extracting a recommended factorcapable of contributing to the enhancement of a fitting degree of therequested model and outputting support data containing data indicatingthe extracted recommended factor to the information processingapparatus.
 13. A model creation support method performed by a computercapable of accessing an information processing apparatus that predictsor analyzes a phenomenon and a factor contributing to the phenomenon tobe a target for prediction or analysis, using a model that is dataindicating a regression equation, the method comprising: acquiring, fromthe information processing apparatus, an identifier of the model, theregression equation of the model in which the phenomenon is representedby an objective variable and the factor is represented by an explanatoryvariable, phenomenon data that includes an objective variable IDidentifying the objective variable of the regression equation andinformation indicating the phenomenon represented by the objectivevariable, factor data that includes an explanatory variable IDidentifying the explanatory variable and information indicating thefactor represented by the explanatory variable of the regressionequation, and evaluation data containing a fitting degree of the model,and accumulating them in a model recording part accessible from thecomputer; referring to the evaluation data on at lease one model in thedata accumulated in the model recording part, thereby extracting anobjective variable ID and an explanatory variable ID identifying anexplanatory variable representing a factor that contributes toenhancement of the fitting degree of the at least one model andrecording the factor value data that associates the phenomenon data ofthe extracted objective variable ID, the factor data of the extractedexplanatory variable ID and data indicating a degree of the contributionin a factor value recording part accessible from the computer; receivinga request for supporting model creation from the information processingapparatus, and further receiving an input of model condition datacontaining data indicating a phenomenon to be a target of a requestedmodel; and matching the data indicating the phenomenon to be a target ofthe requested model indicated by the model condition data with thephenomenon data associated by the factor value data in the factor valuerecording part, thereby extracting a recommended factor capable ofcontributing to the enhancement of a fitting degree of the requestedmodel and outputting support data containing data indicating theextracted recommended factor to the information processing apparatus.